examples of random assignment in real life

Random Assignment in Psychology (Definition + 40 Examples)

Have you ever wondered how researchers discover new ways to help people learn, make decisions, or overcome challenges? A hidden hero in this adventure of discovery is a method called random assignment, a cornerstone in psychological research that helps scientists uncover the truths about the human mind and behavior.

Random Assignment is a process used in research where each participant has an equal chance of being placed in any group within the study. This technique is essential in experiments as it helps to eliminate biases, ensuring that the different groups being compared are similar in all important aspects.

By doing so, researchers can be confident that any differences observed are likely due to the variable being tested, rather than other factors.

In this article, we’ll explore the intriguing world of random assignment, diving into its history, principles, real-world examples, and the impact it has had on the field of psychology.

History of Random Assignment

Stepping back in time, we delve into the origins of random assignment, which finds its roots in the early 20th century.

The pioneering mind behind this innovative technique was Sir Ronald A. Fisher , a British statistician and biologist. Fisher introduced the concept of random assignment in the 1920s, aiming to improve the quality and reliability of experimental research .

His contributions laid the groundwork for the method's evolution and its widespread adoption in various fields, particularly in psychology.

Fisher’s groundbreaking work on random assignment was motivated by his desire to control for confounding variables – those pesky factors that could muddy the waters of research findings.

By assigning participants to different groups purely by chance, he realized that the influence of these confounding variables could be minimized, paving the way for more accurate and trustworthy results.

Early Studies Utilizing Random Assignment

Following Fisher's initial development, random assignment started to gain traction in the research community. Early studies adopting this methodology focused on a variety of topics, from agriculture (which was Fisher’s primary field of interest) to medicine and psychology.

The approach allowed researchers to draw stronger conclusions from their experiments, bolstering the development of new theories and practices.

One notable early study utilizing random assignment was conducted in the field of educational psychology. Researchers were keen to understand the impact of different teaching methods on student outcomes.

By randomly assigning students to various instructional approaches, they were able to isolate the effects of the teaching methods, leading to valuable insights and recommendations for educators.

Evolution of the Methodology

As the decades rolled on, random assignment continued to evolve and adapt to the changing landscape of research.

Advances in technology introduced new tools and techniques for implementing randomization, such as computerized random number generators, which offered greater precision and ease of use.

The application of random assignment expanded beyond the confines of the laboratory, finding its way into field studies and large-scale surveys.

Researchers across diverse disciplines embraced the methodology, recognizing its potential to enhance the validity of their findings and contribute to the advancement of knowledge.

From its humble beginnings in the early 20th century to its widespread use today, random assignment has proven to be a cornerstone of scientific inquiry.

Its development and evolution have played a pivotal role in shaping the landscape of psychological research, driving discoveries that have improved lives and deepened our understanding of the human experience.

Principles of Random Assignment

Delving into the heart of random assignment, we uncover the theories and principles that form its foundation.

The method is steeped in the basics of probability theory and statistical inference, ensuring that each participant has an equal chance of being placed in any group, thus fostering fair and unbiased results.

Basic Principles of Random Assignment

Understanding the core principles of random assignment is key to grasping its significance in research. There are three principles: equal probability of selection, reduction of bias, and ensuring representativeness.

The first principle, equal probability of selection , ensures that every participant has an identical chance of being assigned to any group in the study. This randomness is crucial as it mitigates the risk of bias and establishes a level playing field.

The second principle focuses on the reduction of bias . Random assignment acts as a safeguard, ensuring that the groups being compared are alike in all essential aspects before the experiment begins.

This similarity between groups allows researchers to attribute any differences observed in the outcomes directly to the independent variable being studied.

Lastly, ensuring representativeness is a vital principle. When participants are assigned randomly, the resulting groups are more likely to be representative of the larger population.

This characteristic is crucial for the generalizability of the study’s findings, allowing researchers to apply their insights broadly.

Theoretical Foundation

The theoretical foundation of random assignment lies in probability theory and statistical inference .

Probability theory deals with the likelihood of different outcomes, providing a mathematical framework for analyzing random phenomena. In the context of random assignment, it helps in ensuring that each participant has an equal chance of being placed in any group.

Statistical inference, on the other hand, allows researchers to draw conclusions about a population based on a sample of data drawn from that population. It is the mechanism through which the results of a study can be generalized to a broader context.

Random assignment enhances the reliability of statistical inferences by reducing biases and ensuring that the sample is representative.

Differentiating Random Assignment from Random Selection

It’s essential to distinguish between random assignment and random selection, as the two terms, while related, have distinct meanings in the realm of research.

Random assignment refers to how participants are placed into different groups in an experiment, aiming to control for confounding variables and help determine causes.

In contrast, random selection pertains to how individuals are chosen to participate in a study. This method is used to ensure that the sample of participants is representative of the larger population, which is vital for the external validity of the research.

While both methods are rooted in randomness and probability, they serve different purposes in the research process.

Understanding the theories, principles, and distinctions of random assignment illuminates its pivotal role in psychological research.

This method, anchored in probability theory and statistical inference, serves as a beacon of reliability, guiding researchers in their quest for knowledge and ensuring that their findings stand the test of validity and applicability.

Methodology of Random Assignment

Implementing random assignment in a study is a meticulous process that involves several crucial steps.

The initial step is participant selection, where individuals are chosen to partake in the study. This stage is critical to ensure that the pool of participants is diverse and representative of the population the study aims to generalize to.

Once the pool of participants has been established, the actual assignment process begins. In this step, each participant is allocated randomly to one of the groups in the study.

Researchers use various tools, such as random number generators or computerized methods, to ensure that this assignment is genuinely random and free from biases.

Monitoring and adjusting form the final step in the implementation of random assignment. Researchers need to continuously observe the groups to ensure that they remain comparable in all essential aspects throughout the study.

If any significant discrepancies arise, adjustments might be necessary to maintain the study’s integrity and validity.

Tools and Techniques Used

The evolution of technology has introduced a variety of tools and techniques to facilitate random assignment.

Random number generators, both manual and computerized, are commonly used to assign participants to different groups. These generators ensure that each individual has an equal chance of being placed in any group, upholding the principle of equal probability of selection.

In addition to random number generators, researchers often use specialized computer software designed for statistical analysis and experimental design.

These software programs offer advanced features that allow for precise and efficient random assignment, minimizing the risk of human error and enhancing the study’s reliability.

Ethical Considerations

The implementation of random assignment is not devoid of ethical considerations. Informed consent is a fundamental ethical principle that researchers must uphold.

Informed consent means that every participant should be fully informed about the nature of the study, the procedures involved, and any potential risks or benefits, ensuring that they voluntarily agree to participate.

Beyond informed consent, researchers must conduct a thorough risk and benefit analysis. The potential benefits of the study should outweigh any risks or harms to the participants.

Safeguarding the well-being of participants is paramount, and any study employing random assignment must adhere to established ethical guidelines and standards.

Conclusion of Methodology

The methodology of random assignment, while seemingly straightforward, is a multifaceted process that demands precision, fairness, and ethical integrity. From participant selection to assignment and monitoring, each step is crucial to ensure the validity of the study’s findings.

The tools and techniques employed, coupled with a steadfast commitment to ethical principles, underscore the significance of random assignment as a cornerstone of robust psychological research.

Benefits of Random Assignment in Psychological Research

The impact and importance of random assignment in psychological research cannot be overstated. It is fundamental for ensuring the study is accurate, allowing the researchers to determine if their study actually caused the results they saw, and making sure the findings can be applied to the real world.

Facilitating Causal Inferences

When participants are randomly assigned to different groups, researchers can be more confident that the observed effects are due to the independent variable being changed, and not other factors.

This ability to determine the cause is called causal inference .

This confidence allows for the drawing of causal relationships, which are foundational for theory development and application in psychology.

Ensuring Internal Validity

One of the foremost impacts of random assignment is its ability to enhance the internal validity of an experiment.

Internal validity refers to the extent to which a researcher can assert that changes in the dependent variable are solely due to manipulations of the independent variable , and not due to confounding variables.

By ensuring that each participant has an equal chance of being in any condition of the experiment, random assignment helps control for participant characteristics that could otherwise complicate the results.

Enhancing Generalizability

Beyond internal validity, random assignment also plays a crucial role in enhancing the generalizability of research findings.

When done correctly, it ensures that the sample groups are representative of the larger population, so can allow researchers to apply their findings more broadly.

This representative nature is essential for the practical application of research, impacting policy, interventions, and psychological therapies.

Limitations of Random Assignment

Potential for implementation issues.

While the principles of random assignment are robust, the method can face implementation issues.

One of the most common problems is logistical constraints. Some studies, due to their nature or the specific population being studied, find it challenging to implement random assignment effectively.

For instance, in educational settings, logistical issues such as class schedules and school policies might stop the random allocation of students to different teaching methods .

Ethical Dilemmas

Random assignment, while methodologically sound, can also present ethical dilemmas.

In some cases, withholding a potentially beneficial treatment from one of the groups of participants can raise serious ethical questions, especially in medical or clinical research where participants' well-being might be directly affected.

Researchers must navigate these ethical waters carefully, balancing the pursuit of knowledge with the well-being of participants.

Generalizability Concerns

Even when implemented correctly, random assignment does not always guarantee generalizable results.

The types of people in the participant pool, the specific context of the study, and the nature of the variables being studied can all influence the extent to which the findings can be applied to the broader population.

Researchers must be cautious in making broad generalizations from studies, even those employing strict random assignment.

Practical and Real-World Limitations

In the real world, many variables cannot be manipulated for ethical or practical reasons, limiting the applicability of random assignment.

For instance, researchers cannot randomly assign individuals to different levels of intelligence, socioeconomic status, or cultural backgrounds.

This limitation necessitates the use of other research designs, such as correlational or observational studies , when exploring relationships involving such variables.

Response to Critiques

In response to these critiques, people in favor of random assignment argue that the method, despite its limitations, remains one of the most reliable ways to establish cause and effect in experimental research.

They acknowledge the challenges and ethical considerations but emphasize the rigorous frameworks in place to address them.

The ongoing discussion around the limitations and critiques of random assignment contributes to the evolution of the method, making sure it is continuously relevant and applicable in psychological research.

While random assignment is a powerful tool in experimental research, it is not without its critiques and limitations. Implementation issues, ethical dilemmas, generalizability concerns, and real-world limitations can pose significant challenges.

However, the continued discourse and refinement around these issues underline the method's enduring significance in the pursuit of knowledge in psychology.

By being careful with how we do things and doing what's right, random assignment stays a really important part of studying how people act and think.

Real-World Applications and Examples

Random assignment has been employed in many studies across various fields of psychology, leading to significant discoveries and advancements.

Here are some real-world applications and examples illustrating the diversity and impact of this method:

• Medicine and Health Psychology: Randomized Controlled Trials (RCTs) are the gold standard in medical research. In these studies, participants are randomly assigned to either the treatment or control group to test the efficacy of new medications or interventions.
• Educational Psychology: Studies in this field have used random assignment to explore the effects of different teaching methods, classroom environments, and educational technologies on student learning and outcomes.
• Cognitive Psychology: Researchers have employed random assignment to investigate various aspects of human cognition, including memory, attention, and problem-solving, leading to a deeper understanding of how the mind works.
• Social Psychology: Random assignment has been instrumental in studying social phenomena, such as conformity, aggression, and prosocial behavior, shedding light on the intricate dynamics of human interaction.

Let's get into some specific examples. You'll need to know one term though, and that is "control group." A control group is a set of participants in a study who do not receive the treatment or intervention being tested , serving as a baseline to compare with the group that does, in order to assess the effectiveness of the treatment.

• Smoking Cessation Study: Researchers used random assignment to put participants into two groups. One group received a new anti-smoking program, while the other did not. This helped determine if the program was effective in helping people quit smoking.
• Math Tutoring Program: A study on students used random assignment to place them into two groups. One group received additional math tutoring, while the other continued with regular classes, to see if the extra help improved their grades.
• Exercise and Mental Health: Adults were randomly assigned to either an exercise group or a control group to study the impact of physical activity on mental health and mood.
• Diet and Weight Loss: A study randomly assigned participants to different diet plans to compare their effectiveness in promoting weight loss and improving health markers.
• Sleep and Learning: Researchers randomly assigned students to either a sleep extension group or a regular sleep group to study the impact of sleep on learning and memory.
• Classroom Seating Arrangement: Teachers used random assignment to place students in different seating arrangements to examine the effect on focus and academic performance.
• Music and Productivity: Employees were randomly assigned to listen to music or work in silence to investigate the effect of music on workplace productivity.
• Medication for ADHD: Children with ADHD were randomly assigned to receive either medication, behavioral therapy, or a placebo to compare treatment effectiveness.
• Mindfulness Meditation for Stress: Adults were randomly assigned to a mindfulness meditation group or a waitlist control group to study the impact on stress levels.
• Video Games and Aggression: A study randomly assigned participants to play either violent or non-violent video games and then measured their aggression levels.
• Online Learning Platforms: Students were randomly assigned to use different online learning platforms to evaluate their effectiveness in enhancing learning outcomes.
• Hand Sanitizers in Schools: Schools were randomly assigned to use hand sanitizers or not to study the impact on student illness and absenteeism.
• Caffeine and Alertness: Participants were randomly assigned to consume caffeinated or decaffeinated beverages to measure the effects on alertness and cognitive performance.
• Green Spaces and Well-being: Neighborhoods were randomly assigned to receive green space interventions to study the impact on residents’ well-being and community connections.
• Pet Therapy for Hospital Patients: Patients were randomly assigned to receive pet therapy or standard care to assess the impact on recovery and mood.
• Yoga for Chronic Pain: Individuals with chronic pain were randomly assigned to a yoga intervention group or a control group to study the effect on pain levels and quality of life.
• Flu Vaccines Effectiveness: Different groups of people were randomly assigned to receive either the flu vaccine or a placebo to determine the vaccine’s effectiveness.
• Reading Strategies for Dyslexia: Children with dyslexia were randomly assigned to different reading intervention strategies to compare their effectiveness.
• Physical Environment and Creativity: Participants were randomly assigned to different room setups to study the impact of physical environment on creative thinking.
• Laughter Therapy for Depression: Individuals with depression were randomly assigned to laughter therapy sessions or control groups to assess the impact on mood.
• Financial Incentives for Exercise: Participants were randomly assigned to receive financial incentives for exercising to study the impact on physical activity levels.
• Art Therapy for Anxiety: Individuals with anxiety were randomly assigned to art therapy sessions or a waitlist control group to measure the effect on anxiety levels.
• Natural Light in Offices: Employees were randomly assigned to workspaces with natural or artificial light to study the impact on productivity and job satisfaction.
• School Start Times and Academic Performance: Schools were randomly assigned different start times to study the effect on student academic performance and well-being.
• Horticulture Therapy for Seniors: Older adults were randomly assigned to participate in horticulture therapy or traditional activities to study the impact on cognitive function and life satisfaction.
• Hydration and Cognitive Function: Participants were randomly assigned to different hydration levels to measure the impact on cognitive function and alertness.
• Intergenerational Programs: Seniors and young people were randomly assigned to intergenerational programs to study the effects on well-being and cross-generational understanding.
• Therapeutic Horseback Riding for Autism: Children with autism were randomly assigned to therapeutic horseback riding or traditional therapy to study the impact on social communication skills.
• Active Commuting and Health: Employees were randomly assigned to active commuting (cycling, walking) or passive commuting to study the effect on physical health.
• Mindful Eating for Weight Management: Individuals were randomly assigned to mindful eating workshops or control groups to study the impact on weight management and eating habits.
• Noise Levels and Learning: Students were randomly assigned to classrooms with different noise levels to study the effect on learning and concentration.
• Bilingual Education Methods: Schools were randomly assigned different bilingual education methods to compare their effectiveness in language acquisition.
• Outdoor Play and Child Development: Children were randomly assigned to different amounts of outdoor playtime to study the impact on physical and cognitive development.
• Social Media Detox: Participants were randomly assigned to a social media detox or regular usage to study the impact on mental health and well-being.
• Therapeutic Writing for Trauma Survivors: Individuals who experienced trauma were randomly assigned to therapeutic writing sessions or control groups to study the impact on psychological well-being.
• Mentoring Programs for At-risk Youth: At-risk youth were randomly assigned to mentoring programs or control groups to assess the impact on academic achievement and behavior.
• Dance Therapy for Parkinson’s Disease: Individuals with Parkinson’s disease were randomly assigned to dance therapy or traditional exercise to study the effect on motor function and quality of life.
• Aquaponics in Schools: Schools were randomly assigned to implement aquaponics programs to study the impact on student engagement and environmental awareness.
• Virtual Reality for Phobia Treatment: Individuals with phobias were randomly assigned to virtual reality exposure therapy or traditional therapy to compare effectiveness.
• Gardening and Mental Health: Participants were randomly assigned to engage in gardening or other leisure activities to study the impact on mental health and stress reduction.

Each of these studies exemplifies how random assignment is utilized in various fields and settings, shedding light on the multitude of ways it can be applied to glean valuable insights and knowledge.

Real-world Impact of Random Assignment

Random assignment is like a key tool in the world of learning about people's minds and behaviors. It’s super important and helps in many different areas of our everyday lives. It helps make better rules, creates new ways to help people, and is used in lots of different fields.

Health and Medicine

In health and medicine, random assignment has helped doctors and scientists make lots of discoveries. It’s a big part of tests that help create new medicines and treatments.

By putting people into different groups by chance, scientists can really see if a medicine works.

This has led to new ways to help people with all sorts of health problems, like diabetes, heart disease, and mental health issues like depression and anxiety.

Schools and education have also learned a lot from random assignment. Researchers have used it to look at different ways of teaching, what kind of classrooms are best, and how technology can help learning.

This knowledge has helped make better school rules, develop what we learn in school, and find the best ways to teach students of all ages and backgrounds.

Workplace and Organizational Behavior

Random assignment helps us understand how people act at work and what makes a workplace good or bad.

Studies have looked at different kinds of workplaces, how bosses should act, and how teams should be put together. This has helped companies make better rules and create places to work that are helpful and make people happy.

Environmental and Social Changes

Random assignment is also used to see how changes in the community and environment affect people. Studies have looked at community projects, changes to the environment, and social programs to see how they help or hurt people’s well-being.

This has led to better community projects, efforts to protect the environment, and programs to help people in society.

Technology and Human Interaction

In our world where technology is always changing, studies with random assignment help us see how tech like social media, virtual reality, and online stuff affect how we act and feel.

This has helped make better and safer technology and rules about using it so that everyone can benefit.

The effects of random assignment go far and wide, way beyond just a science lab. It helps us understand lots of different things, leads to new and improved ways to do things, and really makes a difference in the world around us.

From making healthcare and schools better to creating positive changes in communities and the environment, the real-world impact of random assignment shows just how important it is in helping us learn and make the world a better place.

So, what have we learned? Random assignment is like a super tool in learning about how people think and act. It's like a detective helping us find clues and solve mysteries in many parts of our lives.

From creating new medicines to helping kids learn better in school, and from making workplaces happier to protecting the environment, it’s got a big job!

This method isn’t just something scientists use in labs; it reaches out and touches our everyday lives. It helps make positive changes and teaches us valuable lessons.

Whether we are talking about technology, health, education, or the environment, random assignment is there, working behind the scenes, making things better and safer for all of us.

In the end, the simple act of putting people into groups by chance helps us make big discoveries and improvements. It’s like throwing a small stone into a pond and watching the ripples spread out far and wide.

Thanks to random assignment, we are always learning, growing, and finding new ways to make our world a happier and healthier place for everyone!

Related posts:

• 19+ Experimental Design Examples (Methods + Types)
• Cluster Sampling vs Stratified Sampling
• 41+ White Collar Job Examples (Salary + Path)
• 47+ Blue Collar Job Examples (Salary + Path)
• McDonaldization of Society (Definition + Examples)

Reference this article:

About The Author

Free Personality Test

Free Memory Test

Free IQ Test

PracticalPie.com is a participant in the Amazon Associates Program. As an Amazon Associate we earn from qualifying purchases.

Follow Us On:

Youtube Facebook Instagram X/Twitter

Psychology Resources

Developmental

Personality

Relationships

Psychologists

Serial Killers

Psychology Tests

Personality Quiz

Memory Test

Depression test

Type A/B Personality Test

© PracticalPsychology. All rights reserved

Privacy Policy | Terms of Use

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on March 8, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomization.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomized designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors, not research biases like sampling bias or selection bias .

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, other interesting articles, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment and avoid biases.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

• a control group that’s given a placebo (no dosage, to control for a placebo effect ),
• an experimental group that’s given a low dosage,
• a second experimental group that’s given a high dosage.

Random assignment to helps you make sure that the treatment groups don’t differ in systematic ways at the start of the experiment, as this can seriously affect (and even invalidate) your work.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

• participants recruited from cafes are placed in the control group ,
• participants recruited from local community centers are placed in the low dosage experimental group,
• participants recruited from gyms are placed in the high dosage group.

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym-users may tend to engage in more healthy behaviors than people who frequent cafes or community centers, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

Prevent plagiarism. Run a free check.

Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.

While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sampling enhances the external validity or generalizability of your results, because it helps ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

• a control group that receives no intervention.
• an experimental group that has a remote team-building intervention every week for a month.

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

• Random number generator: Use a computer program to generate random numbers from the list for each group.
• Lottery method: Place all numbers individually in a hat or a bucket, and draw numbers at random for each group.
• Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
• Use a dice: When you have three groups, for each number on the list, roll a dice to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomized block design involves placing participants into blocks based on a shared characteristic (e.g., college students versus graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing men and women or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women, etc.). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviors, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers). These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). Random Assignment in Experiments | Introduction & Examples. Scribbr. Retrieved June 24, 2024, from https://www.scribbr.com/methodology/random-assignment/

Is this article helpful?

Pritha Bhandari

Other students also liked, guide to experimental design | overview, steps, & examples, confounding variables | definition, examples & controls, control groups and treatment groups | uses & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

The Definition of Random Assignment According to Psychology

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Materio / Getty Images

Random assignment refers to the use of chance procedures in psychology experiments to ensure that each participant has the same opportunity to be assigned to any given group in a study to eliminate any potential bias in the experiment at the outset. Participants are randomly assigned to different groups, such as the treatment group versus the control group. In clinical research, randomized clinical trials are known as the gold standard for meaningful results.

Simple random assignment techniques might involve tactics such as flipping a coin, drawing names out of a hat, rolling dice, or assigning random numbers to a list of participants. It is important to note that random assignment differs from random selection .

While random selection refers to how participants are randomly chosen from a target population as representatives of that population, random assignment refers to how those chosen participants are then assigned to experimental groups.

Random Assignment In Research

To determine if changes in one variable will cause changes in another variable, psychologists must perform an experiment. Random assignment is a critical part of the experimental design that helps ensure the reliability of the study outcomes.

Researchers often begin by forming a testable hypothesis predicting that one variable of interest will have some predictable impact on another variable.

The variable that the experimenters will manipulate in the experiment is known as the independent variable , while the variable that they will then measure for different outcomes is known as the dependent variable. While there are different ways to look at relationships between variables, an experiment is the best way to get a clear idea if there is a cause-and-effect relationship between two or more variables.

Once researchers have formulated a hypothesis, conducted background research, and chosen an experimental design, it is time to find participants for their experiment. How exactly do researchers decide who will be part of an experiment? As mentioned previously, this is often accomplished through something known as random selection.

Random Selection

In order to generalize the results of an experiment to a larger group, it is important to choose a sample that is representative of the qualities found in that population. For example, if the total population is 60% female and 40% male, then the sample should reflect those same percentages.

Choosing a representative sample is often accomplished by randomly picking people from the population to be participants in a study. Random selection means that everyone in the group stands an equal chance of being chosen to minimize any bias. Once a pool of participants has been selected, it is time to assign them to groups.

By randomly assigning the participants into groups, the experimenters can be fairly sure that each group will have the same characteristics before the independent variable is applied.

Participants might be randomly assigned to the control group , which does not receive the treatment in question. The control group may receive a placebo or receive the standard treatment. Participants may also be randomly assigned to the experimental group , which receives the treatment of interest. In larger studies, there can be multiple treatment groups for comparison.

There are simple methods of random assignment, like rolling the die. However, there are more complex techniques that involve random number generators to remove any human error.

There can also be random assignment to groups with pre-established rules or parameters. For example, if you want to have an equal number of men and women in each of your study groups, you might separate your sample into two groups (by sex) before randomly assigning each of those groups into the treatment group and control group.

Random assignment is essential because it increases the likelihood that the groups are the same at the outset. With all characteristics being equal between groups, other than the application of the independent variable, any differences found between group outcomes can be more confidently attributed to the effect of the intervention.

Example of Random Assignment

Imagine that a researcher is interested in learning whether or not drinking caffeinated beverages prior to an exam will improve test performance. After randomly selecting a pool of participants, each person is randomly assigned to either the control group or the experimental group.

The participants in the control group consume a placebo drink prior to the exam that does not contain any caffeine. Those in the experimental group, on the other hand, consume a caffeinated beverage before taking the test.

Participants in both groups then take the test, and the researcher compares the results to determine if the caffeinated beverage had any impact on test performance.

A Word From Verywell

Random assignment plays an important role in the psychology research process. Not only does this process help eliminate possible sources of bias, but it also makes it easier to generalize the results of a tested sample of participants to a larger population.

Random assignment helps ensure that members of each group in the experiment are the same, which means that the groups are also likely more representative of what is present in the larger population of interest. Through the use of this technique, psychology researchers are able to study complex phenomena and contribute to our understanding of the human mind and behavior.

Lin Y, Zhu M, Su Z. The pursuit of balance: An overview of covariate-adaptive randomization techniques in clinical trials . Contemp Clin Trials. 2015;45(Pt A):21-25. doi:10.1016/j.cct.2015.07.011

Sullivan L. Random assignment versus random selection . In: The SAGE Glossary of the Social and Behavioral Sciences. SAGE Publications, Inc.; 2009. doi:10.4135/9781412972024.n2108

Alferes VR. Methods of Randomization in Experimental Design . SAGE Publications, Inc.; 2012. doi:10.4135/9781452270012

Nestor PG, Schutt RK. Research Methods in Psychology: Investigating Human Behavior. (2nd Ed.). SAGE Publications, Inc.; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

What Is Random Assignment in Psychology?

Categories Research Methods

Random assignment means that every participant has the same chance of being chosen for the experimental or control group. It involves using procedures that rely on chance to assign participants to groups. Doing this means that every participant in a study has an equal opportunity to be assigned to any group.

For example, in a psychology experiment, participants might be assigned to either a control or experimental group. Some experiments might only have one experimental group, while others may have several treatment variations.

Using random assignment means that each participant has the same chance of being assigned to any of these groups.

Table of Contents

How to Use Random Assignment

So what type of procedures might psychologists utilize for random assignment? Strategies can include:

• Flipping a coin
• Assigning random numbers
• Rolling dice
• Drawing names out of a hat

How Does Random Assignment Work?

A psychology experiment aims to determine if changes in one variable lead to changes in another variable. Researchers will first begin by coming up with a hypothesis. Once researchers have an idea of what they think they might find in a population, they will come up with an experimental design and then recruit participants for their study.

Once they have a pool of participants representative of the population they are interested in looking at, they will randomly assign the participants to their groups.

• Control group : Some participants will end up in the control group, which serves as a baseline and does not receive the independent variables.
• Experimental group : Other participants will end up in the experimental groups that receive some form of the independent variables.

By using random assignment, the researchers make it more likely that the groups are equal at the start of the experiment. Since the groups are the same on other variables, it can be assumed that any changes that occur are the result of varying the independent variables.

After a treatment has been administered, the researchers will then collect data in order to determine if the independent variable had any impact on the dependent variable.

Random Assignment vs. Random Selection

It is important to remember that random assignment is not the same thing as random selection , also known as random sampling.

Random selection instead involves how people are chosen to be in a study. Using random selection, every member of a population stands an equal chance of being chosen for a study or experiment.

So random sampling affects how participants are chosen for a study, while random assignment affects how participants are then assigned to groups.

Examples of Random Assignment

Imagine that a psychology researcher is conducting an experiment to determine if getting adequate sleep the night before an exam results in better test scores.

Forming a Hypothesis

They hypothesize that participants who get 8 hours of sleep will do better on a math exam than participants who only get 4 hours of sleep.

Obtaining Participants

The researcher starts by obtaining a pool of participants. They find 100 participants from a local university. Half of the participants are female, and half are male.

Randomly Assign Participants to Groups

The researcher then assigns random numbers to each participant and uses a random number generator to randomly assign each number to either the 4-hour or 8-hour sleep groups.

Conduct the Experiment

Those in the 8-hour sleep group agree to sleep for 8 hours that night, while those in the 4-hour group agree to wake up after only 4 hours. The following day, all of the participants meet in a classroom.

Collect and Analyze Data

Everyone takes the same math test. The test scores are then compared to see if the amount of sleep the night before had any impact on test scores.

Why Is Random Assignment Important in Psychology Research?

Random assignment is important in psychology research because it helps improve a study’s internal validity. This means that the researchers are sure that the study demonstrates a cause-and-effect relationship between an independent and dependent variable.

Random assignment improves the internal validity by minimizing the risk that there are systematic differences in the participants who are in each group.

Key Points to Remember About Random Assignment

• Random assignment in psychology involves each participant having an equal chance of being chosen for any of the groups, including the control and experimental groups.
• It helps control for potential confounding variables, reducing the likelihood of pre-existing differences between groups.
• This method enhances the internal validity of experiments, allowing researchers to draw more reliable conclusions about cause-and-effect relationships.
• Random assignment is crucial for creating comparable groups and increasing the scientific rigor of psychological studies.

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Random Assignment in Experiments

By Jim Frost 4 Comments

Random assignment uses chance to assign subjects to the control and treatment groups in an experiment. This process helps ensure that the groups are equivalent at the beginning of the study, which makes it safer to assume the treatments caused any differences between groups that the experimenters observe at the end of the study.

Huh? That might be a big surprise! At this point, you might be wondering about all of those studies that use statistics to assess the effects of different treatments. There’s a critical separation between significance and causality:

• Statistical procedures determine whether an effect is significant.
• Experimental designs determine how confidently you can assume that a treatment causes the effect.

In this post, learn how using random assignment in experiments can help you identify causal relationships.

Correlation, Causation, and Confounding Variables

Random assignment helps you separate causation from correlation and rule out confounding variables. As a critical component of the scientific method , experiments typically set up contrasts between a control group and one or more treatment groups. The idea is to determine whether the effect, which is the difference between a treatment group and the control group, is statistically significant. If the effect is significant, group assignment correlates with different outcomes.

However, as you have no doubt heard, correlation does not necessarily imply causation. In other words, the experimental groups can have different mean outcomes, but the treatment might not be causing those differences even though the differences are statistically significant.

The difficulty in definitively stating that a treatment caused the difference is due to potential confounding variables or confounders. Confounders are alternative explanations for differences between the experimental groups. Confounding variables correlate with both the experimental groups and the outcome variable. In this situation, confounding variables can be the actual cause for the outcome differences rather than the treatments themselves. As you’ll see, if an experiment does not account for confounding variables, they can bias the results and make them untrustworthy.

Related posts : Understanding Correlation in Statistics , Causation versus Correlation , and Hill’s Criteria for Causation .

Example of Confounding in an Experiment

• Control group: Does not consume vitamin supplements
• Treatment group: Regularly consumes vitamin supplements.

Imagine we measure a specific health outcome. After the experiment is complete, we perform a 2-sample t-test to determine whether the mean outcomes for these two groups are different. Assume the test results indicate that the mean health outcome in the treatment group is significantly better than the control group.

Why can’t we assume that the vitamins improved the health outcomes? After all, only the treatment group took the vitamins.

Related post : Confounding Variables in Regression Analysis

Alternative Explanations for Differences in Outcomes

The answer to that question depends on how we assigned the subjects to the experimental groups. If we let the subjects decide which group to join based on their existing vitamin habits, it opens the door to confounding variables. It’s reasonable to assume that people who take vitamins regularly also tend to have other healthy habits. These habits are confounders because they correlate with both vitamin consumption (experimental group) and the health outcome measure.

Random assignment prevents this self sorting of participants and reduces the likelihood that the groups start with systematic differences.

In fact, studies have found that supplement users are more physically active, have healthier diets, have lower blood pressure, and so on compared to those who don’t take supplements. If subjects who already take vitamins regularly join the treatment group voluntarily, they bring these healthy habits disproportionately to the treatment group. Consequently, these habits will be much more prevalent in the treatment group than the control group.

The healthy habits are the confounding variables—the potential alternative explanations for the difference in our study’s health outcome. It’s entirely possible that these systematic differences between groups at the start of the study might cause the difference in the health outcome at the end of the study—and not the vitamin consumption itself!

If our experiment doesn’t account for these confounding variables, we can’t trust the results. While we obtained statistically significant results with the 2-sample t-test for health outcomes, we don’t know for sure whether the vitamins, the systematic difference in habits, or some combination of the two caused the improvements.

Learn why many randomized clinical experiments use a placebo to control for the Placebo Effect .

Experiments Must Account for Confounding Variables

Your experimental design must account for confounding variables to avoid their problems. Scientific studies commonly use the following methods to handle confounders:

• Use control variables to keep them constant throughout an experiment.
• Statistically control for them in an observational study.
• Use random assignment to reduce the likelihood that systematic differences exist between experimental groups when the study begins.

Let’s take a look at how random assignment works in an experimental design.

Random Assignment Can Reduce the Impact of Confounding Variables

Note that random assignment is different than random sampling. Random sampling is a process for obtaining a sample that accurately represents a population .

Random assignment uses a chance process to assign subjects to experimental groups. Using random assignment requires that the experimenters can control the group assignment for all study subjects. For our study, we must be able to assign our participants to either the control group or the supplement group. Clearly, if we don’t have the ability to assign subjects to the groups, we can’t use random assignment!

Additionally, the process must have an equal probability of assigning a subject to any of the groups. For example, in our vitamin supplement study, we can use a coin toss to assign each subject to either the control group or supplement group. For more complex experimental designs, we can use a random number generator or even draw names out of a hat.

Random Assignment Distributes Confounders Equally

The random assignment process distributes confounding properties amongst your experimental groups equally. In other words, randomness helps eliminate systematic differences between groups. For our study, flipping the coin tends to equalize the distribution of subjects with healthier habits between the control and treatment group. Consequently, these two groups should start roughly equal for all confounding variables, including healthy habits!

Random assignment is a simple, elegant solution to a complex problem. For any given study area, there can be a long list of confounding variables that you could worry about. However, using random assignment, you don’t need to know what they are, how to detect them, or even measure them. Instead, use random assignment to equalize them across your experimental groups so they’re not a problem.

Because random assignment helps ensure that the groups are comparable when the experiment begins, you can be more confident that the treatments caused the post-study differences. Random assignment helps increase the internal validity of your study.

Comparing the Vitamin Study With and Without Random Assignment

Let’s compare two scenarios involving our hypothetical vitamin study. We’ll assume that the study obtains statistically significant results in both cases.

Scenario 1: We don’t use random assignment and, unbeknownst to us, subjects with healthier habits disproportionately end up in the supplement treatment group. The experimental groups differ by both healthy habits and vitamin consumption. Consequently, we can’t determine whether it was the habits or vitamins that improved the outcomes.

Scenario 2: We use random assignment and, consequently, the treatment and control groups start with roughly equal levels of healthy habits. The intentional introduction of vitamin supplements in the treatment group is the primary difference between the groups. Consequently, we can more confidently assert that the supplements caused an improvement in health outcomes.

For both scenarios, the statistical results could be identical. However, the methodology behind the second scenario makes a stronger case for a causal relationship between vitamin supplement consumption and health outcomes.

How important is it to use the correct methodology? Well, if the relationship between vitamins and health outcomes is not causal, then consuming vitamins won’t cause your health outcomes to improve regardless of what the study indicates. Instead, it’s probably all the other healthy habits!

Learn more about Randomized Controlled Trials (RCTs) that are the gold standard for identifying causal relationships because they use random assignment.

Drawbacks of Random Assignment

Random assignment helps reduce the chances of systematic differences between the groups at the start of an experiment and, thereby, mitigates the threats of confounding variables and alternative explanations. However, the process does not always equalize all of the confounding variables. Its random nature tends to eliminate systematic differences, but it doesn’t always succeed.

Sometimes random assignment is impossible because the experimenters cannot control the treatment or independent variable. For example, if you want to determine how individuals with and without depression perform on a test, you cannot randomly assign subjects to these groups. The same difficulty occurs when you’re studying differences between genders.

In other cases, there might be ethical issues. For example, in a randomized experiment, the researchers would want to withhold treatment for the control group. However, if the treatments are vaccinations, it might be unethical to withhold the vaccinations.

Other times, random assignment might be possible, but it is very challenging. For example, with vitamin consumption, it’s generally thought that if vitamin supplements cause health improvements, it’s only after very long-term use. It’s hard to enforce random assignment with a strict regimen for usage in one group and non-usage in the other group over the long-run. Or imagine a study about smoking. The researchers would find it difficult to assign subjects to the smoking and non-smoking groups randomly!

Fortunately, if you can’t use random assignment to help reduce the problem of confounding variables, there are different methods available. The other primary approach is to perform an observational study and incorporate the confounders into the statistical model itself. For more information, read my post Observational Studies Explained .

Read About Real Experiments that Used Random Assignment

I’ve written several blog posts about studies that have used random assignment to make causal inferences. Read studies about the following:

• Flu Vaccinations
• COVID-19 Vaccinations

Sullivan L.  Random assignment versus random selection . SAGE Glossary of the Social and Behavioral Sciences, SAGE Publications, Inc.; 2009.

Share this:

Reader Interactions

November 13, 2019 at 4:59 am

Hi Jim, I have a question of randomly assigning participants to one of two conditions when it is an ongoing study and you are not sure of how many participants there will be. I am using this random assignment tool for factorial experiments. http://methodologymedia.psu.edu/most/rannumgenerator It asks you for the total number of participants but at this point, I am not sure how many there will be. Thanks for any advice you can give me, Floyd

May 28, 2019 at 11:34 am

Jim, can you comment on the validity of using the following approach when we can’t use random assignments. I’m in education, we have an ACT prep course that we offer. We can’t force students to take it and we can’t keep them from taking it either. But we want to know if it’s working. Let’s say that by senior year all students who are going to take the ACT have taken it. Let’s also say that I’m only including students who have taking it twice (so I can show growth between first and second time taking it). What I’ve done to address confounders is to go back to say 8th or 9th grade (prior to anyone taking the ACT or the ACT prep course) and run an analysis showing the two groups are not significantly different to start with. Is this valid? If the ACT prep students were higher achievers in 8th or 9th grade, I could not assume my prep course is effecting greater growth, but if they were not significantly different in 8th or 9th grade, I can assume the significant difference in ACT growth (from first to second testing) is due to the prep course. Yes or no?

May 26, 2019 at 5:37 pm

Nice post! I think the key to understanding scientific research is to understand randomization. And most people don’t get it.

May 27, 2019 at 9:48 pm

Thank you, Anoop!

I think randomness in an experiment is a funny thing. The issue of confounding factors is a serious problem. You might not even know what they are! But, use random assignment and, voila, the problem usually goes away! If you can’t use random assignment, suddenly you have a whole host of issues to worry about, which I’ll be writing about in more detail in my upcoming post about observational experiments!

Comments and Questions Cancel reply

• Best-Selling Books
• Zimbardo Research Fields

The Stanford Prison Experiment

• Heroic Imagination Project (HIP)
• The Shyness Clinic

The Lucifer Effect

Time perspective theory.

• Psychology Definitions

Random Assignment: Psychology Definition, History & Examples

Random assignment is a foundational concept in experimental psychology, serving as a core methodological strategy to ensure the validity of research findings. By randomly allocating participants to different groups, researchers aim to control for extraneous variables, thereby enhancing the internal validity of their studies.

Historically, this technique has its roots in the field’s evolution towards more rigorous scientific methodologies, progressively refining the ways in which psychological phenomena are empirically tested. Various hallmark experiments across cognitive, social, and clinical psychology have employed random assignment to demonstrate causal relationships between variables.

Such examples underscore the significance of the method in disentangling complex behavioral dynamics. This introduction provides an overview of random assignment, tracing its development and illustrating its application through pertinent examples within psychological research.

Table of Contents

Random assignment in psychology refers to the process of randomly assigning participants to different groups in an experiment . This helps ensure that each group is similar and reduces bias, making the study’s results more reliable.

It allows researchers to attribute the effects observed to the independent variable being tested, rather than other factors, increasing the study’s validity.

Historical Background of Random Assignment in Psychology

Random assignment, a fundamental methodology in psychological research, originated in the early 20th century and has since played a crucial role in advancing the field. This approach was developed to enhance the rigor and validity of experimental design by ensuring unbiased distribution of extraneous variables across treatment and control groups.

The concept of random assignment was influenced by the refinement of the scientific method and the desire for objectivity and replicability in psychological studies. Key figures associated with its development include eminent psychologists such as Charles Sanders Peirce, Ronald A. Fisher, and Jerzy Neyman.

One significant event that contributed to the evolution of random assignment was the advent of experimental psychology in the late 19th century. This marked a shift away from relying solely on introspection and subjective methods towards a more rigorous and empirical approach. As psychologists increasingly sought to establish causality in their research, random assignment emerged as a powerful tool to control for potential confounding factors.

In the early 20th century, Fisher and Neyman independently developed statistical techniques that further solidified the importance of random assignment. Fisher’s work on the design of experiments and the analysis of variance, along with Neyman’s contributions to mathematical statistics, laid the foundation for the widespread adoption of random assignment in psychological research.

Significant studies also played a role in shaping the prominence of random assignment. For example, the Stanford Prison Experiment conducted by Philip Zimbardo in 1971 utilized random assignment to assign participants to the roles of prisoners and guards. This study highlighted the ethical considerations and psychological effects of random assignment, sparking discussions and further refinements in its application.

Random assignment is a concept in psychology that is used in everyday life to ensure fairness and eliminate bias. For example, imagine you are organizing a game of dodgeball. To make the teams fair, you could use random assignment by drawing names out of a hat to determine which players will be on each team. This way, everyone has an equal chance of being on either team, and it helps prevent any advantages or disadvantages based on personal abilities.

Another real-life example of random assignment can be found in product testing. Let’s say a company wants to test the effectiveness of a new face cream. They would use random assignment to assign participants to two groups: one group would use the new face cream, and the other group would use a placebo cream. By randomly assigning participants to each group, the researchers can ensure that any differences in results between the two groups are due to the face cream itself and not other factors like age or skin type.

In education, random assignment can also be seen in the allocation of classroom seating. Teachers often use a random assignment method to assign students to different seats at the beginning of the school year. This helps create a fair and balanced learning environment , as students have an equal chance of being seated next to different classmates and forming new relationships.

These examples demonstrate how random assignment is applied in various real-life situations to ensure fairness, eliminate bias, and obtain reliable results. By using random assignment, researchers, organizers, and educators can make more accurate conclusions and decisions based on data that is free from confounding variables.

Related Terms

Several related terms are essential to understand when discussing random assignment in psychological research, including variables, control groups, and random sampling. These terms are closely linked as they all play crucial roles in the design and implementation of experiments.

Variables are the elements that researchers aim to measure, manipulate, or control in their study. They can be classified into independent variables, which are the presumed causes, and dependent variables, which are the observed effects. For example, in a study investigating the effects of a new medication on anxiety , the independent variable would be the medication, while the dependent variable would be the level of anxiety.

Control groups, on the other hand, serve as a standard or baseline for comparison against the experimental group. They do not receive the experimental treatment, allowing researchers to determine whether the treatment has a genuine effect. In the medication study mentioned earlier, the control group would receive a placebo or an existing medication for anxiety, while the experimental group would receive the new medication.

Random sampling is another important term in psychological research, although it is distinct from random assignment. Random sampling refers to the process of selecting participants from a larger population to be included in the study. It aims to ensure that the sample is representative of the population and that the findings can be generalized.

Random assignment, on the other hand, deals with how participants are then allocated to different groups within the experiment. It ensures that participants have an equal chance of being assigned to either the control or experimental group, minimizing the influence of confounding variables.

In understanding the concept of random assignment in psychology, it is essential to consult reputable sources, studies, and publications that have contributed knowledge to this field. These academically credible references provide a solid foundation for further reading and contribute to a comprehensive understanding of random assignment.

Scholarly journals, such as the Journal of Experimental Psychology: General, the Journal of Personality and Social Psychology, and the Journal of Abnormal Psychology, often publish research articles that explore the application and importance of random assignment in psychological research. These articles undergo rigorous peer-review processes, ensuring that the information presented is of high quality and meets academic standards.

Seminal research articles, such as those by Fisher (1935) and Neyman (1923), have made significant contributions to the understanding and use of random assignment in experimental design. These articles provide historical perspectives and methodological insights that have shaped the field of psychology and continue to inform current research practices.

Authoritative texts, like ‘Experimental and Quasi-Experimental Designs for Generalized Causal Inference’ by Shadish, Cook, and Campbell (2002), offer comprehensive overviews of experimental design, including random assignment. These texts provide in-depth explanations, theoretical frameworks, and practical guidelines for implementing random assignment in psychological research.

RECOMMENDED POSTS

• Stay Connected
• Terms Of Use

Random Assignment in Psychology (Intro for Students)

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

Learn about our Editorial Process

Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Random assignment is a research procedure used to randomly assign participants to different experimental conditions (or ‘groups’). This introduces the element of chance, ensuring that each participant has an equal likelihood of being placed in any condition group for the study.

It is absolutely essential that the treatment condition and the control condition are the same in all ways except for the variable being manipulated.

Using random assignment to place participants in different conditions helps to achieve this.

It ensures that those conditions are the same in regards to all potential confounding variables and extraneous factors .

Why Researchers Use Random Assignment

Researchers use random assignment to control for confounds in research.

Confounds refer to unwanted and often unaccounted-for variables that might affect the outcome of a study. These confounding variables can skew the results, rendering the experiment unreliable.

For example, below is a study with two groups. Note how there are more ‘red’ individuals in the first group than the second:

There is likely a confounding variable in this experiment explaining why more red people ended up in the treatment condition and less in the control condition. The red people might have self-selected, for example, leading to a skew of them in one group over the other.

Ideally, we’d want a more even distribution, like below:

To achieve better balance in our two conditions, we use randomized sampling.

Fact File: Experiments 101

Random assignment is used in the type of research called the experiment.

An experiment involves manipulating the level of one variable and examining how it affects another variable. These are the independent and dependent variables :

• Independent Variable: The variable manipulated is called the independent variable (IV)
• Dependent Variable: The variable that it is expected to affect is called the dependent variable (DV).

The most basic form of the experiment involves two conditions: the treatment and the control .

• The Treatment Condition: The treatment condition involves the participants being exposed to the IV.
• The Control Condition: The control condition involves the absence of the IV. Therefore, the IV has two levels: zero and some quantity.

Researchers utilize random assignment to determine which participants go into which conditions.

Methods of Random Assignment

There are several procedures that researchers can use to randomly assign participants to different conditions.

1. Random number generator

There are several websites that offer computer-generated random numbers. Simply indicate how many conditions are in the experiment and then click. If there are 4 conditions, the program will randomly generate a number between 1 and 4 each time it is clicked.

2. Flipping a coin

If there are two conditions in an experiment, then the simplest way to implement random assignment is to flip a coin for each participant. Heads means being assigned to the treatment and tails means being assigned to the control (or vice versa).

3. Rolling a die

Rolling a single die is another way to randomly assign participants. If the experiment has three conditions, then numbers 1 and 2 mean being assigned to the control; numbers 3 and 4 mean treatment condition one; and numbers 5 and 6 mean treatment condition two.

4. Condition names in a hat

In some studies, the researcher will write the name of the treatment condition(s) or control on slips of paper and place them in a hat. If there are 4 conditions and 1 control, then there are 5 slips of paper.

The researcher closes their eyes and selects one slip for each participant. That person is then assigned to one of the conditions in the study and that slip of paper is placed back in the hat. Repeat as necessary.

There are other ways of trying to ensure that the groups of participants are equal in all ways with the exception of the IV. However, random assignment is the most often used because it is so effective at reducing confounds.

Read About More Methods and Examples of Random Assignment Here

Potential Confounding Effects

Random assignment is all about minimizing confounding effects.

Here are six types of confounds that can be controlled for using random assignment:

• Individual Differences: Participants in a study will naturally vary in terms of personality, intelligence, mood, prior knowledge, and many other characteristics. If one group happens to have more people with a particular characteristic, this could affect the results. Random assignment ensures that these individual differences are spread out equally among the experimental groups, making it less likely that they will unduly influence the outcome.
• Temporal or Time-Related Confounds: Events or situations that occur at a particular time can influence the outcome of an experiment. For example, a participant might be tested after a stressful event, while another might be tested after a relaxing weekend. Random assignment ensures that such effects are equally distributed among groups, thus controlling for their potential influence.
• Order Effects: If participants are exposed to multiple treatments or tests, the order in which they experience them can influence their responses. Randomly assigning the order of treatments for different participants helps control for this.
• Location or Environmental Confounds: The environment in which the study is conducted can influence the results. One group might be tested in a noisy room, while another might be in a quiet room. Randomly assigning participants to different locations can control for these effects.
• Instrumentation Confounds: These occur when there are variations in the calibration or functioning of measurement instruments across conditions. If one group’s responses are being measured using a slightly different tool or scale, it can introduce a confound. Random assignment can ensure that any such potential inconsistencies in instrumentation are equally distributed among groups.
• Experimenter Effects: Sometimes, the behavior or expectations of the person administering the experiment can unintentionally influence the participants’ behavior or responses. For instance, if an experimenter believes one treatment is superior, they might unconsciously communicate this belief to participants. Randomly assigning experimenters or using a double-blind procedure (where neither the participant nor the experimenter knows the treatment being given) can help control for this.

Random assignment helps balance out these and other potential confounds across groups, ensuring that any observed differences are more likely due to the manipulated independent variable rather than some extraneous factor.

Limitations of the Random Assignment Procedure

Although random assignment is extremely effective at eliminating the presence of participant-related confounds, there are several scenarios in which it cannot be used.

• Ethics: The most obvious scenario is when it would be unethical. For example, if wanting to investigate the effects of emotional abuse on children, it would be unethical to randomly assign children to either received abuse or not.  Even if a researcher were to propose such a study, it would not receive approval from the Institutional Review Board (IRB) which oversees research by university faculty.
• Practicality: Other scenarios involve matters of practicality. For example, randomly assigning people to specific types of diet over a 10-year period would be interesting, but it would be highly unlikely that participants would be diligent enough to make the study valid. This is why examining these types of subjects has to be carried out through observational studies . The data is correlational, which is informative, but falls short of the scientist’s ultimate goal of identifying causality.
• Small Sample Size: The smaller the sample size being assigned to conditions, the more likely it is that the two groups will be unequal. For example, if you flip a coin many times in a row then you will notice that sometimes there will be a string of heads or tails that come up consecutively. This means that one condition may have a build-up of participants that share the same characteristics. However, if you continue flipping the coin, over the long-term, there will be a balance of heads and tails. Unfortunately, how large a sample size is necessary has been the subject of considerable debate (Bloom, 2006; Shadish et al., 2002).

“It is well known that larger sample sizes reduce the probability that random assignment will result in conditions that are unequal” (Goldberg, 2019, p. 2).

Applications of Random Assignment

The importance of random assignment has been recognized in a wide range of scientific and applied disciplines (Bloom, 2006).

Random assignment began as a tool in agricultural research by Fisher (1925, 1935). After WWII, it became extensively used in medical research to test the effectiveness of new treatments and pharmaceuticals (Marks, 1997).

Today it is widely used in industrial engineering (Box, Hunter, and Hunter, 2005), educational research (Lindquist, 1953; Ong-Dean et al., 2011)), psychology (Myers, 1972), and social policy studies (Boruch, 1998; Orr, 1999).

One of the biggest obstacles to the validity of an experiment is the confound. If the group of participants in the treatment condition are substantially different from the group in the control condition, then it is impossible to determine if the IV has an affect or if the confound has an effect.

Thankfully, random assignment is highly effective at eliminating confounds that are known and unknown. Because each participant has an equal chance of being placed in each condition, they are equally distributed.

There are several ways of implementing random assignment, including flipping a coin or using a random number generator.

Random assignment has become an essential procedure in research in a wide range of subjects such as psychology, education, and social policy.

Alferes, V. R. (2012). Methods of randomization in experimental design . Sage Publications.

Bloom, H. S. (2008). The core analytics of randomized experiments for social research. The SAGE Handbook of Social Research Methods , 115-133.

Boruch, R. F. (1998). Randomized controlled experiments for evaluation and planning. Handbook of applied social research methods , 161-191.

Box, G. E., Hunter, W. G., & Hunter, J. S. (2005). Design of experiments: Statistics for Experimenters: Design, Innovation and Discovery.

Dehue, T. (1997). Deception, efficiency, and random groups: Psychology and the gradual origination of the random group design. Isis , 88 (4), 653-673.

Fisher, R.A. (1925). Statistical methods for research workers (11th ed. rev.). Oliver and Boyd: Edinburgh.

Fisher, R. A. (1935). The Design of Experiments. Edinburgh: Oliver and Boyd.

Goldberg, M. H. (2019). How often does random assignment fail? Estimates and recommendations. Journal of Environmental Psychology , 66 , 101351.

Jamison, J. C. (2019). The entry of randomized assignment into the social sciences. Journal of Causal Inference , 7 (1), 20170025.

Lindquist, E. F. (1953). Design and analysis of experiments in psychology and education . Boston: Houghton Mifflin Company.

Marks, H. M. (1997). The progress of experiment: Science and therapeutic reform in the United States, 1900-1990 . Cambridge University Press.

Myers, J. L. (1972). Fundamentals of experimental design (2nd ed.). Allyn & Bacon.

Ong-Dean, C., Huie Hofstetter, C., & Strick, B. R. (2011). Challenges and dilemmas in implementing random assignment in educational research. American Journal of Evaluation , 32 (1), 29-49.

Orr, L. L. (1999). Social experiments: Evaluating public programs with experimental methods . Sage.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Quasi-experiments: interrupted time-series designs. Experimental and quasi-experimental designs for generalized causal inference , 171-205.

Stigler, S. M. (1992). A historical view of statistical concepts in psychology and educational research. American Journal of Education , 101 (1), 60-70.

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Theory of Planned Behavior Examples

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

AP®︎/College Statistics

Course: ap®︎/college statistics   >   unit 6.

• Statistical significance of experiment

Random sampling vs. random assignment (scope of inference)

• Conclusions in observational studies versus experiments
• Finding errors in study conclusions

• (Choice A)   Just the residents involved in Hilary's study. A Just the residents involved in Hilary's study.
• (Choice B)   All residents in Hilary's town. B All residents in Hilary's town.
• (Choice C)   All residents in Hilary's country. C All residents in Hilary's country.
• (Choice A)   Yes A Yes
• (Choice B)   No B No
• (Choice A)   Just the residents in Hilary's study. A Just the residents in Hilary's study.

Want to join the conversation?

• Upvote Button navigates to signup page
• Downvote Button navigates to signup page
• Flag Button navigates to signup page

Experimental Design: Types, Examples & Methods

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Related Articles

Research Methodology

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

3 Real-World Examples of Using Instrumental Variables

The instrumental variable approach is a method to identify the causal effect of a treatment on an outcome of interest by controlling for unobserved confounding between them.

A valid instrumental variable, Z, is one that influences the outcome, Y, through the treatment, X, without being related to the confounding variable, C, as shown in the following diagram:

Therefore, the effect of the instrumental variable Z on the outcome Y can be used to estimate the effect of the treatment X on the outcome Y bypassing the confounding effect of C.

For more details, see: 7 Different Ways to Control for Confounding .

Example 1: Measuring the effect of smoking during pregnancy on birth weight

Smoking during pregnancy is correlated with low birth weight. But this association is confounded by all sort of unobserved (and hard to measure) maternal characteristics that complicate the relationship between smoking and low birth weight.

Evans and Ringel used cigarette taxes as an instrumental variable to study the causal effect of smoking on birth weight, as shown in the diagram below:

Cigarette taxes is a valid instrumental variable since:

• Cigarette taxes alter the smoking behavior of pregnant women (the instrumental variable Z affects the treatment X).
• Cigarettes taxes can only affect birth weight through the change in the smoking behavior of pregnant women (Z has no direct influence on the outcome Y).
• Cigarette taxes are not affected by the characteristics of the pregnant women (Z is not caused by the confounder C).

Therefore, cigarette taxes can be considered as a source of exogenous variation in the smoking behavior of pregnant women, resembling random assignment of pregnant women to either smoking or not.

Study results:

The authors concluded that an increase in the cigarette tax rates has a beneficial impact on birth weight.

Their estimate of the effect of smoking during pregnancy on birth weight was close to that of a randomized controlled trial, and larger in magnitude than the estimates obtained from other observational studies that used more classical methods of confounding control.

Example 2: Measuring the effect of smoking cessation on weight gain

In order to measure the effect of smoking cessation on weight gain, studies typically compare the average weight gain of quitters to that of continuing smokers while controlling for confounding factors such as age, gender, and initial body weight.

These studies, however, may still be biased since the relationship between smoking cessation and weight gain could be confounded by a long list of unmeasurable and unknown factors.

Eisenberg and Quinn used the instrumental variable approach on data from a randomized controlled trial that randomized smokers to either receive a smoking cessation intervention or not.

Here’s a causal diagram that represents relationships between variables in their study:

Random assignment of smokers to the intervention is a valid instrumental variable since:

• This random assignment affects the probability of quitting smoking (Z affects X).
• This random assignment can only affect weight gain through smoking cessation (Z has no direct influence on Y).
• This random assignment is not affected by any type of confounding (Z is not caused by C).

Therefore, random assignment of smokers to a smoking cessation intervention can be considered as a source of exogenous variation in smoking cessation, allowing us to study the effect of the latter on weight gain bypassing any confounding effect.

Study results

The authors concluded that smoking cessation caused twice the increase in weight as estimated by other studies using suboptimal methods to control confounding.

Example 3: Measuring the effect of heart attack treatment intensity on survival

Patients with a heart attack receive different treatments according to their health status (such as: the presence of other diseases, the severity of the case, etc.). So, if we want to study the causal effect of heart attack treatment intensity on survival we will have to face confounding by all kinds of patient characteristics.

Controlling for all these observable and unobservable characteristics is a real pain that can be avoided by using the instrumental variable approach.

In their study, McClellan and colleagues used the patient proximity to different types of hospitals as an instrumental variable to study the effect of treatment intensity on survival, as shown in the following diagram:

Patient proximity to hospitals is a valid instrumental variable since:

• The patient proximity to a certain type of hospital determines the type and intensity of the received treatment (Z affects X).
• The patient proximity to a certain type of hospital affects the outcome only through the treatment received. In other words, it does not have a direct effect on survival (Z affects Y only through X).
• The patient proximity to a certain type of hospital is not affected by the health status of the patient (Z is not related to C).

So, differential distances approximately randomize patients to receive different intensities of treatments, and therefore can be used to estimate the causal effect of the treatment intensity on survival.

The authors concluded that, when it comes to heart attacks, using a more aggressive treatment is not as efficacious as the timing of that treatment.

Further reading

• 7 Different Ways to Control for Confounding
• Front-Door Criterion to Adjust for Unmeasured Confounding
• 5 Real-World Examples of Confounding [With References]
• 8 Types of Treatment Effects Explained (with Examples)
• List of All Biases in Research (Sorted by Popularity)

Running Randomized Control Trials (RCTs) in the Real World

Introduction

While a variety of study designs can be used to estimate the population health impacts of social interventions, randomized control trials (RCTs) are uniquely well-suited to assess  causal relationships  between an intervention and outcomes. These RCTs, which call for random assignment of study participants to treatment and control groups, are often considered a  gold standard approach  when feasible, because randomization helps ensure study groups are comparable to each other and reduces the chance that factors outside the intervention could affect outcomes. In controlled environments, RCTs produce exceptionally reliable results; but the real world is not a highly controlled environment. Consequently conducting an RCT under real world conditions is a complex process with numerous challenges. E4A has funded several individual and cluster-randomized control trials, and the shared experiences of our grantees offer insights into how researchers and practitioners can navigate these complex issues to produce high quality evidence. These insights can also help policy makers better understand and act on evidence from RCTs. 

Common Challenges to Navigate

Recruitment and retention.  Even in laboratory settings there are barriers to enrollment, such as lack of interest, reliable transportation, or paid time off to participate in study activities. RCTs run in the real world face additional challenges. For example, in the context of school-based RCTs, school administrators need assurance of tangible benefits to their school. For app-based enrollment, data privacy concerns can discourage participant consent.

Compensation may be offered to offset some of these barriers, but in addition to legal and ethical considerations, it can be difficult to anticipate how much compensation is needed to effectively incentivize participation without inadvertently creating suspicion, which may deter participants.  

If too few people participate, the study risks being  underpowered , meaning it may not be possible to detect program effects, even if they exist. Attrition, or loss of study participants over the duration of the study, poses another threat to  power  and can bias results if there are differences in which participants are lost to follow up. 

Tradeoffs between research and program or policy objectives .  RCTs in the real world are often forced to balance priorities between the study rigor and the objective(s) of program or policy implementors. For instance, RCTs require a  control group , which may raise concerns among practitioners over withholding or delaying services or resources to people who are in need. Addressing research and program priorities simultaneously requires a coordinated approach across multiple stakeholders.

Unexpected shocks.  RCTs are frequently conducted in lab settings under highly controlled conditions. But in the real world any variety of external shocks – from natural disasters to political or economic turmoil – can disrupt the conditions under which an intervention is being implemented and evaluated. COVID-19 is an extreme example of an unexpected shock that has upended the settings in which real world RCTs are being carried out. 

Possible Approaches

Despite the obstacles that real world dynamics pose to conducting RCTs, there are practices that can help mitigate these challenges and enhance the strength and credibility of RCT findings.

Productive partnerships . Researchers who have strong rapport with program implementors have found that these close partnerships make it easier to navigate competing priorities and deal with changes. When faced with unexpected shocks, researchers and program implementors are able to better navigate program disruptions together.

Partner(s) on the ground.  Ensuring that there is a team member or point person who can engage directly with study participants and provide coordination can greatly improve recruitment and retention. For instance, E4A supported researchers evaluating the country’s first city-led guaranteed income pilot, the  Stockton Economic Empowerment Demonstration (SEED) , attribute much of their success in recruiting and retaining participants to having a dedicated team member who is in constant communication with program participants.

Contingency Planning . Planning ahead for attrition or potential shocks when determining recruitment targets can help ensure that studies have adequate sample sizes and power, even in the face of real world challenges.

Putting Evidence into Practice

Strong evidence is needed to understand which interventions drive change. RCTs are widely considered the most rigorous way to assess causal relationships between an intervention and outcomes; but even a well-designed RCT can face challenges when implemented in real world conditions. To increase their confidence in research findings, decision makers weighing the credibility of an RCT should be attuned to common pitfalls, and whether best practices were applied in carrying out a study.

Tools & Resources

• (Em)powering Population Health Decision-Making: Maximizing the Potential of Social Interventions Research
• Determining the Causal Effects of Interventions: Alternative Methods for Evaluation

About the author(s)

Natalie DiRocco, MPH,  supports the application and grant management activities of E4A. In her role, she manages application reviews, provides technical assistance to applicants, monitors grantee deliverables, and maintains a grantee network.

Statistics Made Easy

10 Examples of Random Variables in Real Life

A random variable is a variable whose possible values are outcomes of a random process.

There are two types of random variables:

• Discrete : Can take on only a countable number of distinct values like 0, 1, 2, 3, 50, 100, etc.
• Continuous : Can take on an infinite number of possible values like 0.03, 1.2374553, etc.

In this article we share 10 examples of random variables in different real-life situations.

Example 1: Number of Items Sold (Discrete)

One example of a discrete random variable is the number of items sold at a store on a certain day.

Using historical sales data, a store could create a probability distribution that shows how likely it is that they sell a certain number of items in a day.

For example:

The probability that they sell 0 items is .004, the probability that they sell 1 item is .023, etc.

Example 2: Number of Customers (Discrete)

Another example of a discrete random variable is the number of customers that enter a shop on a given day.

Using historical data, a shop could create a probability distribution that shows how likely it is that a certain number of customers enter the store.

Example 3: Number of Defective Products (Discrete)

Another example of a discrete random variable is the number of defective products produced per batch by a certain manufacturing plant.

Using historical data on defective products, a plant could create a probability distribution that shows how likely it is that a certain number of products will be defective in a given batch.

Example 4: Number of Traffic Accidents (Discrete)

Another example of a discrete random variable is the number of traffic accidents that occur in a specific city on a given day.

Using historical data, a police department could create a probability distribution that shows how likely it is that a certain number of accidents occur on a given day.

Example 5: Number of Home Runs (Discrete)

Another example of a discrete random variable is the number of home runs hit by a certain baseball team in a game.

Using historical data, sports analysts could create a probability distribution that shows how likely it is that the team hits a certain number of home runs in a given game.

Example 6: Marathon Time (Continuous)

One example of a continuous random variable is the marathon time of a given runner.

This is an example of a continuous random variable because it can take on an infinite number of values.

For example, a runner might complete the marathon in 3 hours 20 minutes 12.0003433 seconds. Or they may complete the marathon in 4 hours 6 minutes 2.28889 seconds, etc.

In this scenario, we could use historical marathon times to create a probability distribution that tells us the probability that a given runner finishes between a certain time interval.

Example 7: Interest Rate (Continuous)

Another example of a continuous random variable is the interest rate of loans in a certain country.

This is a continuous random variable because it can take on an infinite number of values. For example, a loan could have an interest rate of 3.5%, 3.765555%, 4.00095%, etc.

In this scenario, we could use historical interest rates to create a probability distribution that tells us the probability that a loan will have an interest rate within a certain interval.

Example 8: Animal Weight (Continuous)

Another example of a continuous random variable is the weight of a certain animal like a dog.

This is a continuous random variable because it can take on an infinite number of values. For example, a dog might weigh 30.333 pounds, 50.340999 pounds, 60.5 pounds, etc.

In this case, we could collect data on the weight of dogs and create a probability distribution that tells us the probability that a randomly selected dog weighs between two different amounts.

Example 9: Plant Height (Continuous)

Another example of a continuous random variable is the height of a certain species of plant.

This is a continuous random variable because it can take on an infinite number of values. For example, a plant might have a height of 6.5555 inches, 8.95 inches, 12.32426 inches, etc.

In this case, we could collect data on the height of this species of plant and create a probability distribution that tells us the probability that a randomly selected plant has a height between two different values.

Example 10: Distance Traveled (Continuous)

Another example of a continuous random variable is the distance traveled by a certain wolf during migration season.

This is a continuous random variable because it can take on an infinite number of values. For example, a wolf may travel 40.335 miles, 80.5322 miles, 105.59 miles, etc.

In this scenario, we could collect data on the distance traveled by wolves and create a probability distribution that tells us the probability that a randomly selected wolf will travel within a certain distance interval.

Additional Resources

The following tutorials provide additional information about variables in statistics:

Introduction to Random Variables What Are i.i.d. Random Variables? What Are Levels of an Independent Variable?

Featured Posts

Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Join the Statology Community

Sign up to receive Statology's exclusive study resource: 100 practice problems with step-by-step solutions. Plus, get our latest insights, tutorials, and data analysis tips straight to your inbox!

By subscribing you accept Statology's Privacy Policy.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Random Assignment in Experiments | Introduction & Examples

Random Assignment in Experiments | Introduction & Examples

Published on 6 May 2022 by Pritha Bhandari . Revised on 13 February 2023.

In experimental research, random assignment is a way of placing participants from your sample into different treatment groups using randomisation.

With simple random assignment, every member of the sample has a known or equal chance of being placed in a control group or an experimental group. Studies that use simple random assignment are also called completely randomised designs .

Random assignment is a key part of experimental design . It helps you ensure that all groups are comparable at the start of a study: any differences between them are due to random factors.

Table of contents

Why does random assignment matter, random sampling vs random assignment, how do you use random assignment, when is random assignment not used, frequently asked questions about random assignment.

Random assignment is an important part of control in experimental research, because it helps strengthen the internal validity of an experiment.

In experiments, researchers manipulate an independent variable to assess its effect on a dependent variable, while controlling for other variables. To do so, they often use different levels of an independent variable for different groups of participants.

This is called a between-groups or independent measures design.

You use three groups of participants that are each given a different level of the independent variable:

• A control group that’s given a placebo (no dosage)
• An experimental group that’s given a low dosage
• A second experimental group that’s given a high dosage

Random assignment to helps you make sure that the treatment groups don’t differ in systematic or biased ways at the start of the experiment.

If you don’t use random assignment, you may not be able to rule out alternative explanations for your results.

• Participants recruited from pubs are placed in the control group
• Participants recruited from local community centres are placed in the low-dosage experimental group
• Participants recruited from gyms are placed in the high-dosage group

With this type of assignment, it’s hard to tell whether the participant characteristics are the same across all groups at the start of the study. Gym users may tend to engage in more healthy behaviours than people who frequent pubs or community centres, and this would introduce a healthy user bias in your study.

Although random assignment helps even out baseline differences between groups, it doesn’t always make them completely equivalent. There may still be extraneous variables that differ between groups, and there will always be some group differences that arise from chance.

Most of the time, the random variation between groups is low, and, therefore, it’s acceptable for further analysis. This is especially true when you have a large sample. In general, you should always use random assignment in experiments when it is ethically possible and makes sense for your study topic.

Prevent plagiarism, run a free check.

Random sampling and random assignment are both important concepts in research, but it’s important to understand the difference between them.

Random sampling (also called probability sampling or random selection) is a way of selecting members of a population to be included in your study. In contrast, random assignment is a way of sorting the sample participants into control and experimental groups.

While random sampling is used in many types of studies, random assignment is only used in between-subjects experimental designs.

Some studies use both random sampling and random assignment, while others use only one or the other.

Random sampling enhances the external validity or generalisability of your results, because it helps to ensure that your sample is unbiased and representative of the whole population. This allows you to make stronger statistical inferences .

You use a simple random sample to collect data. Because you have access to the whole population (all employees), you can assign all 8,000 employees a number and use a random number generator to select 300 employees. These 300 employees are your full sample.

Random assignment enhances the internal validity of the study, because it ensures that there are no systematic differences between the participants in each group. This helps you conclude that the outcomes can be attributed to the independent variable .

• A control group that receives no intervention
• An experimental group that has a remote team-building intervention every week for a month

You use random assignment to place participants into the control or experimental group. To do so, you take your list of participants and assign each participant a number. Again, you use a random number generator to place each participant in one of the two groups.

To use simple random assignment, you start by giving every member of the sample a unique number. Then, you can use computer programs or manual methods to randomly assign each participant to a group.

• Random number generator: Use a computer program to generate random numbers from the list for each group.
• Lottery method: Place all numbers individually into a hat or a bucket, and draw numbers at random for each group.
• Flip a coin: When you only have two groups, for each number on the list, flip a coin to decide if they’ll be in the control or the experimental group.
• Use a dice: When you have three groups, for each number on the list, roll a die to decide which of the groups they will be in. For example, assume that rolling 1 or 2 lands them in a control group; 3 or 4 in an experimental group; and 5 or 6 in a second control or experimental group.

This type of random assignment is the most powerful method of placing participants in conditions, because each individual has an equal chance of being placed in any one of your treatment groups.

Random assignment in block designs

In more complicated experimental designs, random assignment is only used after participants are grouped into blocks based on some characteristic (e.g., test score or demographic variable). These groupings mean that you need a larger sample to achieve high statistical power .

For example, a randomised block design involves placing participants into blocks based on a shared characteristic (e.g., college students vs graduates), and then using random assignment within each block to assign participants to every treatment condition. This helps you assess whether the characteristic affects the outcomes of your treatment.

In an experimental matched design , you use blocking and then match up individual participants from each block based on specific characteristics. Within each matched pair or group, you randomly assign each participant to one of the conditions in the experiment and compare their outcomes.

Sometimes, it’s not relevant or ethical to use simple random assignment, so groups are assigned in a different way.

When comparing different groups

Sometimes, differences between participants are the main focus of a study, for example, when comparing children and adults or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.

In this type of study, the characteristic of interest (e.g., gender) is an independent variable, and the groups differ based on the different levels (e.g., men, women). All participants are tested the same way, and then their group-level outcomes are compared.

When it’s not ethically permissible

When studying unhealthy or dangerous behaviours, it’s not possible to use random assignment. For example, if you’re studying heavy drinkers and social drinkers, it’s unethical to randomly assign participants to one of the two groups and ask them to drink large amounts of alcohol for your experiment.

When you can’t assign participants to groups, you can also conduct a quasi-experimental study . In a quasi-experiment, you study the outcomes of pre-existing groups who receive treatments that you may not have any control over (e.g., heavy drinkers and social drinkers).

These groups aren’t randomly assigned, but may be considered comparable when some other variables (e.g., age or socioeconomic status) are controlled for.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomisation. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalisability of your results, while random assignment improves the internal validity of your study.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a die to randomly assign participants to groups.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2023, February 13). Random Assignment in Experiments | Introduction & Examples. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/random-assignment-experiments/

Is this article helpful?

Pritha Bhandari

Other students also liked, a quick guide to experimental design | 5 steps & examples, controlled experiments | methods & examples of control, control groups and treatment groups | uses & examples.

10 Real-Life Examples Of Random Variables To Understand It Better

A random variable is a variable that represents the possible outcomes of a random process or experiment. It is a mathematical object that can take on different numerical values depending on the outcome of the random process.

While there is a substantial difference between probability and statistics, however, in both of these, a random variable is used to model a random process and represent the possible outcomes of that process. For example, if you were flipping a coin multiple times, the number of heads that appear would be a random variable. The possible values of the random variable would be 0, 1, 2, and so on, depending on how many heads appeared.

The importance of random variables lies in their ability to help us understand and make predictions about real-world phenomena that involve uncertainty. The probability of each possible value of a random variable is determined by the underlying probability distribution of the random process. This allows us to make predictions about the likelihood of different outcomes occurring and make decisions based on this information.

Explaining random variables through real-life examples

Random variables are also important in many fields, such as finance, engineering, and medicine, where decision-making and risk analysis are important. For example, in finance, random variables are used to model the returns on investments, allowing investors to make informed decisions about where to allocate their resources. 

In engineering, random variables are used to model the reliability of complex systems, allowing engineers to design systems that are robust and reliable. In medicine, random variables are used to model the effectiveness of medical treatments, allowing doctors to make informed decisions about which treatments to use for different patients.  

Hence, here are a few practical examples of random variables that can help the little learners understand the concept better, and retain it for a longer time:

1. The amount of money a person wins in a lottery . This is a random variable because the amount can vary depending on the number of winners and the size of the prize pool. By using a random variable to model the potential range of values that the winnings could take on, a person can better understand their chances of winning and make more informed decisions about whether to buy a lottery ticket.

2. The time it takes for a person to run a mile . This is a random variable because the time can vary depending on the person’s fitness level and other factors. By using a random variable to model the potential range of values that the running time could take on, a person can better understand their own performance and make more informed decisions about how to improve their fitness.

3. The weight of a newborn baby . This is a random variable because the weight can vary depending on the baby’s genetics and other factors. By using a random variable to model the potential range of values that the baby’s weight could take on, doctors and parents can better understand the health of the baby and make more informed decisions about the baby’s care.

4.  The number of customers who visit a store in a day . This is a random variable because the number of customers can vary each day and is not fixed. By using a random variable to model the potential range of values that the number of customers could take on, a store owner can better understand their business and make more informed decisions about how to manage the store.

5. The stock price of a company . This is a random variable because the price can vary depending on various factors such as market conditions and the performance of the company. By using a random variable to model the potential range of values that the stock price could take on, investors can better understand the risks and potential rewards of investing in the company and make more informed decisions about whether to buy or sell the stock.

6.   The number of seconds it takes for a computer to complete a certain task . This is a random variable because the time can vary depending on the complexity of the task and the capabilities of the computer. By using a random variable to model the potential range of values that the task completion time could take on, computer engineers can better understand the performance of the computer and make more informed decisions about how to improve it.

7.   The number of cars that pass through a particular intersection in a given hour is a random variable, as it can be affected by factors such as the time of day, the weather, and the presence of traffic lights or other control measures.

8.   The amount of electricity consumed by a household in a month is a random variable, as it can vary based on factors such as the number of occupants in the household, the appliances and devices used, and the weather.

9.   The success or failure of a new product launch is a random variable, as it can be influenced by a variety of factors such as market demand, competition, and the effectiveness of the product’s marketing campaign.

10.   The outcome of a sports game is a random variable, as it is determined by the performance of the competing teams, which can be affected by factors such as injuries, strategy, and luck.

Random variables: How to understand these brain twisters facilely

Understanding random variables can be challenging, but there are a few tips that can help make the concept more accessible:

 1.   Start by understanding the basic concept of a random process or experiment. A random process is one in which the outcome is uncertain and cannot be predicted with certainty. For example, flipping a coin or rolling a die are both examples of random processes. 

2.   Next, understand that a random variable is a mathematical object that represents the possible outcomes of a random process. For example, in the coin-flipping example, the number of heads that appear is a random variable.

3.   Understand that random variables can be either discrete or continuous. A discrete random variable is one that can take on only a finite or countable number of values, such as the number of heads that appear when flipping a coin. A continuous random variable is one that can take on any value within a specified range, such as the height of a person. 

4.   Familiarize yourself with the different types of probability distributions that are commonly used to model random variables. For example, the binomial distribution is used to model the number of successes in a sequence of independent trials, while the normal distribution is used to model continuous data that are symmetrically distributed around a mean. 

5.   Practice working with random variables and probability distributions through examples and exercises. This will help you gain a better understanding of how these concepts work in practice and develop your ability to apply them to real-world problems.

Overall, understanding random variables can be challenging, but with practice and a strong foundation in the underlying concepts, you can develop the skills and knowledge necessary to work with these brain twisters.  

In conclusion, random variables are a fundamental concept in probability and statistics, and both concepts can be inculcated through a few games and activities . They are used to model random processes and represent the possible outcomes of these processes. By understanding the properties of a random variable, we can make predictions about the likelihood of certain outcomes occurring and make decisions based on this information.

 Random variables are important in many fields, such as finance, engineering, and medicine, where decision-making and risk analysis are important. They are also useful in many everyday situations where we need to understand and manage uncertainty.

 Hence, the study of random variables is a valuable tool for understanding and making predictions about real-world phenomena that involve uncertainty. It is an important area of study in probability and statistics, and its applications are widespread and varied.

An engineer, Maths expert, Online Tutor and animal rights activist. In more than 5+ years of my online teaching experience, I closely worked with many students struggling with dyscalculia and dyslexia. With the years passing, I learned that not much effort being put into the awareness of this learning disorder. Students with dyscalculia often misunderstood for having  just a simple math fear. This is still an underresearched and understudied subject. I am also the founder of  Smartynote -‘The notepad app for dyslexia’, 

Leave a Comment Cancel reply

You must be logged in to post a comment.

16 Critical Thinking Examples in Real Life

What is critical thinking.

While making your academic assignments or thesis, you are required to do some research and analyze various things, or for making a career decision or any other decision you are required to think of all pros and cons of that decision. Well, the most important thing that helps us to effectively take these decisions is what we call critical thinking. Critical thinking is very important in both personal and professional life. The process of critical thinking involves the analysis of the various facts and figures in a particular situation before straightaway acting on that situation. Critical thinking demands keen observation, creativity, problem-solving skills, which helps the individual to thoroughly evaluate the gathered information and then use this available information as a guide to making accurate decisions. From doing academic works or regular activities to solving various large scale problems, critical thinking is required in everyday life. In this article, we will learn about some real-life examples where critical thinking plays an important role.

Critical Thinking Examples in Real Life

1. critical thinking in problem solving.

Suppose your manager asks you to find an effective solution to a problem that is affecting the business. What would be your first step? Like most people, you may also start looking for potential solutions to deal with that situation. Well, one requires the use of critical thinking here. Before looking for the solution one needs to take a step back and try to understand the cause of the problem first. One should ask for the opinions of the other people that how does this particular problem impact them and the overall business. If you arrive at a solution, you should not only just rely on one solution, instead, you should always have various backup plans in case the first solution does not work as expected. Most people feel that they are great at problem-solving, but if one is not following all these above discussed steps before making a final judgement, he/she is not a critical thinker. Critical thinking allows people to find the best possible solution to any problem. Critical thinking is an important factor of problem-solving skills, one needs to look at any situation from multiple perspectives because in some cases, your decisions not only impact you but also the people in your surrounding.

2. Critical Thinking in Analysing Risks

Risk assessment is another important factor, which requires the use of critical thinking. Risk assessment is required in various sectors, from children analysing the impact of eating junk food on their health to large businesses in analysing the impact of certain policies on the growth of the company. Let us understand the implication of critical thinking in analysing the risks with some examples.

3. Critical Thinking in Data Analysis

Whether analysing the performance of the children in the schools or analysing the business growth of a multi-national company, the skill of data analysis is very crucial. In today’s era, almost every sector demands experts that can accurately evaluate the available data or information and draw out effective conclusions from it. With the rise in technology, the various tasks of the data analysis such as finding profit and loss, creating balance sheets, and issuing invoices are done with the help of various software, but it does not mean that human skill is not required. Various kinds of software can just convert a large amount of data into some simpler and readable format, but it is the critical thinking of the humans that is required to effectively interpret the data and apply the obtained insight for the benefits. The data analysis can even help us to estimate the future trends and potential risks of taking any decisions.

4. Critical Thinking in Hiring Employees

The ability to objectively view any situation without getting influenced by your personal beliefs or thoughts is one of the important characteristics of critical thinking. In business, the hiring managers require critical thinking to evaluate a large number of resume’s to choose the suitable candidates for the required position. Critical thinking here enables the hiring managers not to hire a candidate on the basis of various factors like gender, age, religion or country, these factors may influence the hiring managers unconsciously. The hiring manager may tend to choose the candidate on his/her subjective beliefs if he/she does not use critical thinking. Hence, critical thinking can help HR’s to hire the best employees that may eventually lead to the growth of the company.

5. Promoting the Teamwork

In a team, every individual is unique and has his/her different ideas to tackle the proposed problem. It is the responsibility of the team leader to understand the perspective of each member and encourage them to work collectively to solve the common problem. You may find the opinion of the other members of your team as ineffective, but instead of straightway denying their opinions one should logically analyse their suggestions and try to put your point of view regarding the problem in an effective and calm manner. If the team leader does not use critical thinking, instead, he/she boost his/her opinions on others, the team is sure to collapse.

6. Critical Thinking in Self-Evaluation

Critical thinking plays a major role in self-evaluation. The knowledge of critical thinking skills allows you to accurately analyse your performance by controlling various subjective biases. People should always evaluate their reactions towards any situation and the way they think, this may help them to get a deep insight into their thought processes, hence improving their thinking abilities to take accurate decisions. Self-evaluation is very important in professional life too. Suppose your manager has set a new target for the company. Every employee is thus required to analyse his/her contribution to the company and try to accomplish the set target. If you know your contribution to the company, it will help you to analyse your performance, and you can try to improve your performance in the areas where you lag.

7. Critical Thinking in Choosing the Career

Almost all of us face various dilemmas in our lives such as choosing the stream, the type of job, choosing between the regular college degree or the online programme. Whatever you choose, every option has its pros and cons. However, critical thinking allows us to accurately weigh the positives and negatives of each option and choose the one that offers more benefits than drawbacks. The best way to do this is to make a list of the pros and the cons and then analyse. Well, this is not just limited to choosing the career path, it can be used in other situations also such as professionally, and financially. One can list the pros and cons of selecting to work in a specific company or choosing the right insurance plan. It is often seen that our choices are greatly influenced by the choices of our friends or known, but one should understand that every individual’s beliefs, desires, and ambitions are different so, if the particular carrear or job is best for the others it does not mean that it would be the best option for you also. Hence, to choose the right carrear path, one requires critical thinking.

8. Critical Thinking in Time Management

Time is the most valuable asset that we have, hence utilizing it appropriately is very crucial. Critical thinking in time management helps you to wisely plan your schedule according to the importance of the particular task or the activity. For example, if the task to which you devote most of your time, is not giving you much return then you need to reconsider your schedule and should devote more time to the tasks that give you high returns.

9. Critical Thinking in Analysing the Fake News

Suppose, one of your friends shares a piece of news with you. Do you bother to analyse that whether this piece of news is real or not? Many of us just believe in the news and shares this with others too without thinking that this can be fake news too. A study conducted by Stanford University showed that around 82 per cent of the teenagers failed to distinguish between the real news and the advertisement with the ‘sponsored content’ label. This problem arises because the standard education curriculum does not emphasise much on critical thinking skills much because of the assumption that critical thinking is inbuilt in every person. By introducing certain lessons or activities that may help to increase the knowledge or overall thinking skills, the critical thinking of the children can be improved. Well, it is also seen that not only children, but adults also fall for these fake news and articles that circulate on various social media platforms. Before believing any piece of information, one should think of various questions like the source of the publication, the intention of the article, the author of the article, and the agenda behind the article. Critical thinking helps us to precisely evaluate any information before straightway believing it.

10. Critical Thinking in Distinguishing between Right and Wrong

Most people, especially teenagers are very much conscious about what their friends or relatives think of their behaviour. You may have had been through the situation, wherein if your friends think that certain behaviour is cool then you start acting in that way to fit in your friend’s circle without even considering that what you are doing is good or bad, and is your actions are related to your beliefs or not? One should understand that if a certain behaviour seems cool to some people, it may also seem bad to some others. One should not change his/her actions depending upon the approval of certain people, rather one should look at the broader aspect and should deeply analyse that whether their actions are morally right or wrong.

11. Critical Thinking in Decoding Fashion Trends

Nowadays, some people are so crazy about following the latest fashion trends, they start following every trend that some popular actor, actress, or fashion influencer suggest. If you are a critical thinker you may have had thought of the questions like why the particular trend that was so popular a few years back seems foolish now? why does a particular trend that does not even look good is so popular? Do the particular fashion trend that suits the other person suits yourself or not? Critical thinking helps people from falling victim to the bandwagon fallacy; it is fallacy in which people starts believing a particular thing or idea as good or bad if the majority of the population thinks so. Fashion trends are a common example of bandwagon fallacy.

12. Critical Thinking in Choosing the Suitable Diet and Exercise

You must have heard of various types of diets such as the Keto diet, Whole 30 diet, Gluten-free diet, Vegan diet and so on. It seems complex to choose the diet that is best for you. What people usually do is that they search online, go through several videos and choose the diet that showed the best results to the person in the video. Well, this is not the right approach, choosing the best diet for yourself requires critical thinking. People who use critical thinking evaluate the pros and cons of the particular diet on their own body, they generally ask about the suitable diet from professional dieticians rather than just following the advice of a random person online. Like choosing a suitable diet, choosing a suitable exercise also demands critical thinking. For example, What are your goals? How can you achieve this? At what time you can do exercise? Do you have any injuries that may get affected by the particular exercise? People who use critical thinking tend to ask all these questions, and then by utilizing the knowledge they have and the following routine for a few weeks, and by analyzing the results they are getting from it, they finally plan a proper schedule for them.

13. Critical Thinking in Online Shopping

In today’s digital era, online shopping is preferred by most people. However, there are various tactics and psychological tricks such as the anchoring effect , Stroop effect , and Serial position effect that are used by the various e-commerce websites, which makes the customers buy more things or things that they don’t even need. Critical thinking can help people to smartly buy items without falling victim to all these effects or tactics. While making the purchase you should focus on the price that you are paying for the particular item rather than the discount you are getting on that item because the chances are that the price that you are paying for that item is not worth paying even after the discount.

14. Critical Thinking in Job Search

Critical thinking plays an important role in the Job search. If you are applying for a job, you may consider the following points to get the desired job.

Use of Keywords in Resume: One should always understand the job post and its requirements before straightaway applying for the job. It is important to update your resume according to the job and add some keywords (mentioned in the job requirements) into your resume to get the job. If you possess some critical thinking skills such as problem-solving, analytical, communication, or creativity skills, it is better to put that in your resume. However, one should always restrain from adding any random critical thinking skills that you do not possess.

Cover Letter: Hiring managers receive hundreds of resumes daily, hence the chances that they will read every resume are quite less. Well, you can make your resume different from others by adding a good cover letter. You can add some of the critical skills that you have to your resume, it is better to explain a little about the tasks or activities where you showed these skills in your previous jobs or work experiences rather than just simply writing the skill. This assures the recruiter that you are not randomly writing the skills and you possess these qualities.

Interviews: Nowadays, some interviewers present the interviewees with hypothetical stories to check their critical thinking skills. You may be asked to explain what you think of the given situation or your first reaction after looking at the given image. You are required to solve any random problem, and then you have to explain to the recruiter about your thought processes. The interviewer here is more focused on the way you reach the conclusion rather than the conclusion itself. Your thought process helps the interviewer to analyse and evaluate the way you approach various problems

15. Critical Thinking While Driving

Imagine you are driving on a busy road and your phone starts ringing. It’s an urgent call that you have to pick. What would you do? Would you pick up the call and risk yourself into an accident or stop your car on the roadside to take the call. Critical thinking helps you to make accurate decisions while driving, it includes finding the right place to park your car, analysing whether you can pass the car through that narrow street or not, or how to handle if any animal suddenly comes in front of your car. Hence, critical thinking is must require skill in driving.

16. Critical Thinking in Business

Critical thinking is one of the most important things that the owner of the business needs to possess. One has to make several important decisions, effectively communicate with the clients, hire suitable employees, take certain risks, and deal with several ups and downs in the business, and much more; all these things require critical thinking.

Related Posts

Psychology: Definition, Types, Perspectives

5 Everyday Life Examples Of Schemas

Conformity & Asch Experiment

Cognitive Bias Examples

8 Examples of Physiological Needs (Maslow’s Hierarchy)

Examples of Ray in Real Life

Great post! I’ve been trying to apply critical thinking to my life, and these examples are a great way to start.

critical thinking is what anyone of us should have in spoiled world

Add Comment Cancel Reply

• Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers
• Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand
• OverflowAI GenAI features for Teams
• OverflowAPI Train & fine-tune LLMs
• Labs The future of collective knowledge sharing
• About the company Visit the blog

Collectives™ on Stack Overflow

Find centralized, trusted content and collaborate around the technologies you use most.

Q&A for work

Connect and share knowledge within a single location that is structured and easy to search.

Get early access and see previews of new features.

What is a real life example of The Las Vegas Algorithm?

I understand the Algorithm but can't seem to find how it's implemented in real life? Anyone have any examples for a better understanding?

• 1 On a side note, I don't understand why my (and many other peoples) questions get downvoted. I follow all of the question guidelines. –  TomD Commented Sep 22, 2018 at 17:02
• Example: Get random prime number : while true: sample random-number; if prime(random-number): return random-number . This will never fail, but the runtime is theoretically unlimited. In some variation, this is used in asymmetric cryptography without people noticing it. Also make sure to process wiki's comment (see your tag montecarlo): Las Vegas algorithms can be contrasted with Monte Carlo algorithms –  sascha Commented Sep 22, 2018 at 17:08
• 1 quora.com/… , found by googling "las vegas algorithm" examples . In answer to your side note comment: I'm not your downvoter, but hovering over the down-pointing vote button below the score produces a pop-up that says "This question does not show any research effort; it is unclear or not useful". Evidently somebody thought that applies. –  pjs Commented Sep 22, 2018 at 18:12
• Another thing which might make the question clearer is offering a one-sentence explanation of what you mean by "the Las Vegas algorithm" in this context. As normally used, the phrase "Las Vegas algorithm" refers not to a single algorithm but rather a feature of a variety of otherwise unrelated algorithms. Similarly, there are "dynamic programming algorithms", "greedy algorithms", "Monte Carlo algorithms", and so on; in none of these cases would it be meaningful to speak of "the X algorithm" as though there were only one such thing.) –  rici Commented Sep 22, 2018 at 20:02

As Wikipedia says in its article about Las Vegas algorithms , a simple example of a Las Vegas algorithm is randomised quicksort ; another simple example is rejection sampling . A more complicated example (linked from the NIST Dictionary of Algorithms site) is an algorithm for finding an order-preserving minimal perfect hash , published in 1992 by Czech, Havas and Majewski.

Another example with a certain amount of practical implementation is the cuckoo hash insertion algorithm

(For reference, a Las Vegas algorithm is a kind of randomised algorithm which is guaranteed to produce a correct answer when it terminates, but which is not guaranteed to terminate in any fixed timespan. Useful Las Vegas algorithms offer good expected running time.)

Your Answer

Reminder: Answers generated by artificial intelligence tools are not allowed on Stack Overflow. Learn more

Sign up or log in

Post as a guest.

Required, but never shown

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy .

Not the answer you're looking for? Browse other questions tagged algorithm random montecarlo or ask your own question .

• Featured on Meta
• Upcoming sign-up experiments related to tags
• Policy: Generative AI (e.g., ChatGPT) is banned
• The [lib] tag is being burninated
• What makes a homepage useful for logged-in users

Hot Network Questions

• How can I take apart a bookshelf?
• Old book about a man who finds an abandoned house with a portal to another world
• Next date in the future such that all 8 digits of MM/DD/YYYY are all different and the product of MM, DD and YY is equal to YYYY
• Can I get a refund for ICE due to cancelled regional bus service?
• Correlation for Small Dataset?
• À + infinitive at start of sentence
• What is the meaning of '"It's nart'ral" in "Pollyanna" by Eleanor H. Porter?
• How is Victor Timely a variant of He Who Remains in the 19th century?
• Is it consistent with ZFC that the real line is approachable by sets with no accumulation points?
• How do you say "living being" in Classical Latin?
• Weird behavior by car insurance - is this legit?
• Is arxiv strictly for new stuff?
• Is it better to show fake sympathy to maintain a good atmosphere?
• What's Wrong With My Math - Odds of 3 Cards of the Same Suit When Drawing 10 Cards
• Does it matter if a fuse is on a positive or negative voltage?
• How to make D&D easier for kids?
• What is the term for when a hyperlink maliciously opens different URL from URL displayed when hovered over?
• Would the category of directed sets be better behaved with the empty set included, or excluded?
• Can I tell a MILP solver to prefer solutions with fewer fractions?
• Cleaning chain a few links at a time
• Is there a way to non-destructively test whether an Ethernet cable is pure copper or copper-clad aluminum (CCA)?
• Is there any other reason to stockpile minerals aside preparing for war?
• Less ridiculous way to prove that an Ascii character compares equal with itself in Coq
• Is it possible to complete a Phd on your own?