control in an science experiment

Controlled Experiment

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This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating 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.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

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 that 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 participating in each condition.

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.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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Controlled Experiment

Reviewed by: BD Editors

Controlled Experiment Definition

A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable , and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or stabilize their effects on the subject. In biology, a controlled experiment often includes restricting the environment of the organism being studied. This is necessary to minimize the random effects of the environment and the many variables that exist in the wild.

In a controlled experiment, the study population is often divided into two groups. One group receives a change in a certain variable, while the other group receives a standard environment and conditions. This group is referred to as the control group , and allows for comparison with the other group, known as the experimental group . Many types of controls exist in various experiments, which are designed to ensure that the experiment worked, and to have a basis for comparison. In science, results are only accepted if it can be shown that they are statistically significant . Statisticians can use the difference between the control group and experimental group and the expected difference to determine if the experiment supports the hypothesis , or if the data was simply created by chance.

Examples of Controlled Experiment

Music preference in dogs.

Do dogs have a taste in music? You might have considered this, and science has too. Believe it or not, researchers have actually tested dog’s reactions to various music genres. To set up a controlled experiment like this, scientists had to consider the many variables that affect each dog during testing. The environment the dog is in when listening to music, the volume of the music, the presence of humans, and even the temperature were all variables that the researches had to consider.

In this case, the genre of the music was the independent variable. In other words, to see if dog’s change their behavior in response to different kinds of music, a controlled experiment had to limit the interaction of the other variables on the dogs. Usually, an experiment like this is carried out in the same location, with the same lighting, furniture, and conditions every time. This ensures that the dogs are not changing their behavior in response to the room. To make sure the dogs don’t react to humans or simply the noise of the music, no one else can be in the room and the music must be played at the same volume for each genre. Scientist will develop protocols for their experiment, which will ensure that many other variables are controlled.

This experiment could also split the dogs into two groups, only testing music on one group. The control group would be used to set a baseline behavior, and see how dogs behaved without music. The other group could then be observed and the differences in the group’s behavior could be analyzed. By rating behaviors on a quantitative scale, statistics can be used to analyze the difference in behavior, and see if it was large enough to be considered significant. This basic experiment was carried out on a large number of dogs, analyzing their behavior with a variety of different music genres. It was found that dogs do show more relaxed and calm behaviors when a specific type of music plays. Come to find out, dogs enjoy reggae the most.

Scurvy in Sailors

In the early 1700s, the world was a rapidly expanding place. Ships were being built and sent all over the world, carrying thousands and thousands of sailors. These sailors were mostly fed the cheapest diets possible, not only because it decreased the costs of goods, but also because fresh food is very hard to keep at sea. Today, we understand that lack of essential vitamins and nutrients can lead to severe deficiencies that manifest as disease. One of these diseases is scurvy.

Scurvy is caused by a simple vitamin C deficiency, but the effects can be brutal. Although early symptoms just include general feeling of weakness, the continued lack of vitamin C will lead to a breakdown of the blood cells and vessels that carry the blood. This results in blood leaking from the vessels. Eventually, people bleed to death internally and die. Before controlled experiments were commonplace, a simple physician decided to tackle the problem of scurvy. James Lind, of the Royal Navy, came up with a simple controlled experiment to find the best cure for scurvy.

He separated sailors with scurvy into various groups. He subjected them to the same controlled condition and gave them the same diet, except one item. Each group was subjected to a different treatment or remedy, taken with their food. Some of these remedies included barley water, cider and a regiment of oranges and lemons. This created the first clinical trial , or test of the effectiveness of certain treatments in a controlled experiment. Lind found that the oranges and lemons helped the sailors recover fast, and within a few years the Royal Navy had developed protocols for growing small leafy greens that contained high amounts of vitamin C to feed their sailors.

Related Biology Terms

• Field Experiment – An experiment conducted in nature, outside the bounds of total control.
• Independent Variable – The thing in an experiment being changed or manipulated by the experimenter to see effects on the subject.
• Controlled Variable – A thing that is normalized or standardized across an experiment, to remove it from having an effect on the subject being studied.
• Control Group – A group of subjects in an experiment that receive no independent variable, or a normalized amount, to provide comparison.

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Methodology

• Control Variables | What Are They & Why Do They Matter?

Control Variables | What Are They & Why Do They Matter?

Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s objectives , but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests). Control variables can help prevent research biases like omitted variable bias from affecting your results.

Table of contents

Why do control variables matter, how do you control a variable, control variable vs. control group, other interesting articles, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest and helps avoid research bias .

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results and affect the reliability of your arguments.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

• Marital status

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There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational studies or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook use more screen time during the day, and this might influence how alert they are in your study.

Standardized procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables , you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

• To control for diet, fresh and frozen meals are delivered to participants three times a day.
• To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
• To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other types of variables .

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

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.

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• Null hypothesis
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• Control groups
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A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

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• Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

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You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

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12.7: Controls in Experiments

Chapter 1: understanding statistics, chapter 2: summarizing and visualizing data, chapter 3: measure of central tendency, chapter 4: measures of variation, chapter 5: measures of relative standing, chapter 6: probability distributions, chapter 7: estimates, chapter 8: distributions, chapter 9: hypothesis testing, chapter 10: analysis of variance, chapter 11: correlation and regression, chapter 12: statistics in practice.

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Controls in an experiment are elements that are held constant and not affected by independent variables. Controls are essential for unbiased and accurate measurement of the dependent variables in response to the treatment.

For example, patients reporting in a hospital with high-grade fever, breathing difficulty, cough, cold, and severe body pain are suspected of COVID infection. But it is  also possible that other respiratory infection causes the same symptoms. So, the doctor recommends a COVID test.

The patient's nasal swabs are collected, and the  COVID test is performed. In addition, a control sample is maintained that does not have COVID viral RNA. This type of control is also called negative control. It helps to prevent false positive reports in patients' samples.

A positive control is another commonly used type of control in an experiment. Unlike the negative control, the positive control contains an actual sample - the viral RNA. This helps to match the presence of viral RNA in the test samples, and it validates the procedure and accuracy of the test.

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a control group that receives an inactive treatment but is otherwise managed exactly as the other groups. The control group helps researchers balance the effects of being in an experiment with the effects of the active treatments.

In clinical or diagnostic procedures, positive controls are included to validate the test results. The positive controls would show the expected result if the test had worked as expected. A negative control does not contain the main ingredient or treatment but includes everything else. For example, in a COVID RT-PCR test, a negative sample does not include the viral DNA. Experiments often use positive and negative controls to prevent or avoid false positives and false negative reports. In

This text is adapted from Openstax, Introductory Statistics, Section 1.4, Experimental Design and Ethics

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Why control an experiment?

John s torday.

1 Department of Pediatrics, Harbor‐UCLA Medical Center, Torrance, CA, USA

František Baluška

2 IZMB, University of Bonn, Bonn, Germany

Empirical research is based on observation and experimentation. Yet, experimental controls are essential for overcoming our sensory limits and generating reliable, unbiased and objective results.

We made a deliberate decision to become scientists and not philosophers, because science offers the opportunity to test ideas using the scientific method. And once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment. In theory, this seems trivial, but in practice, it is often difficult. But where and when did this concept of controlling an experiment start? It is largely attributed to Roger Bacon, who emphasized the use of artificial experiments to provide additional evidence for observations in his Novum Organum Scientiarum in 1620. Other philosophers took up the concept of empirical research: in 1877, Charles Peirce redefined the scientific method in The Fixation of Belief as the most efficient and reliable way to prove a hypothesis. In the 1930s, Karl Popper emphasized the necessity of refuting hypotheses in The Logic of Scientific Discoveries . While these influential works do not explicitly discuss controls as an integral part of experiments, their importance for generating solid and reliable results is nonetheless implicit.

… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.

But the scientific method based on experimentation and observation has come under criticism of late in light of the ever more complex problems faced in physics and biology. Chris Anderson, the editor of Wired Magazine, proposed that we should turn to statistical analysis, machine learning, and pattern recognition instead of creating and testing hypotheses, based on the Informatics credo that if you cannot answer the question, you need more data. However, this attitude subsumes that we already have enough data and that we just cannot make sense of it. This assumption is in direct conflict with David Bohm's thesis that there are two “Orders”, the Explicate and Implicate 1 . The Explicate Order is the way in which our subjective sensory systems perceive the world 2 . In contrast, Bohm's Implicate Order would represent the objective reality beyond our perception. This view—that we have only a subjective understanding of reality—dates back to Galileo Galilei who, in 1623, criticized the Aristotelian concept of absolute and objective qualities of our sensory perceptions 3 and to Plato's cave allegory that reality is only what our senses allow us to see.

The only way for systematically overcoming the limits of our sensory apparatus and to get a glimpse of the Implicate Order is through the scientific method, through hypothesis‐testing, controlled experimentation. Beyond the methodology, controlling an experiment is critically important to ensure that the observed results are not just random events; they help scientists to distinguish between the “signal” and the background “noise” that are inherent in natural and living systems. For example, the detection method for the recent discovery of gravitational waves used four‐dimensional reference points to factor out the background noise of the Cosmos. Controls also help to account for errors and variability in the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated background signals from the assay or measurement. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation.

Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself. But beyond the practical issues to avoid procedural and material artifacts, there is an underlying philosophical question. The need for experimental controls is a subliminal recognition of the relative and subjective nature of the Explicate Order. It requires controls as “reference points” in order to transcend it, and to approximate the Implicate Order.

This is similar to Peter Rowlands’ 4 dictum that everything in the Universe adds up to zero, the universal attractor in mathematics. Prior to the introduction of zero, mathematics lacked an absolute reference point similar to a negative or positive control in an experiment. The same is true of biology, where the cell is the reference point owing to its negative entropy: It appears as an attractor for the energy of its environment. Hence, there is a need for careful controls in biology: The homeostatic balance that is inherent to life varies during the course of an experiment and therefore must be precisely controlled to distinguish noise from signal and approximate the Implicate Order of life.

P  < 0.05 tacitly acknowledges the explicate order

Another example of the “subjectivity” of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than or equal to 5% as statistically significant; that is, there is a less than 0.05 probability that the effect is random. The efficacy of this arbitrary convention has been debated for decades; suffice to say that despite questioning the validity of that convention, a P value of < 0.05 reflects our acceptance of the subjectivity of our perception of reality.

… controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

Thus, if we do away with hypothesis‐testing science in favor of informatics based on data and statistics—referring to Anderson's suggestion—it reflects our acceptance of the noise in the system. However, mere data analysis without any underlying hypothesis is tantamount to “garbage in‐garbage out”, in contrast to well‐controlled imaginative experiments to separate the wheat from the chaff. Albert Einstein was quoted as saying that imagination was more important than knowledge.

The ultimate purpose of the scientific method is to understand ourselves and our place in Nature. Conventionally, we subscribe to the Anthropic Principle, that we are “in” this Universe, whereas the Endosymbiosis Theory, advocated by Lynn Margulis, stipulates that we are “of” this Universe as a result of the assimilation of the physical environment. According to this theory, the organism endogenizes external factors to make them physiologically “useful”, such as iron as the core of the hemoglobin molecule, or ancient bacteria as mitochondria.

… there is a fundamental difference between knowing via believing and knowing based on empirical research.

By applying the developmental mechanism of cell–cell communication to phylogeny, we have revealed the interrelationships between cells and explained evolution from its origin as the unicellular state to multicellularity via cell–cell communication. The ultimate outcome of this research is that consciousness is the product of cellular processes and cell–cell communication in order to react to the environment and better anticipate future events 5 , 6 . Consciousness is an essential prerequisite for transcending the Explicate Order toward the Implicate Order via cellular sensory and cognitive systems that feed an ever‐expanding organismal knowledge about both the environment and itself.

It is here where the empirical approach to understanding nature comes in with its emphasis that knowledge comes only from sensual experience rather than innate ideas or traditions. In the context of the cell or higher systems, knowledge about the environment can only be gained by sensing and analyzing the environment. Empiricism is similar to an equation in which the variables and terms form a product, or a chemical reaction, or a biological process where the substrates, aka sensory data, form products, that is, knowledge. However, it requires another step—imagination, according to Albert Einstein—to transcend the Explicate Order in order to gain insight into the Implicate Order. Take for instance, Dmitri Ivanovich Mendeleev's Periodic Table of Elements: his brilliant insight was not just to use Atomic Number to organize it, but also to consider the chemical reactivities of the Elements by sorting them into columns. By introducing chemical reactivity to the Periodic Table, Mendeleev provided something like the “fourth wall” in Drama, which gives the audience an omniscient, god‐like perspective on what is happening on stage.

The capacity to transcend the subjective Explicate Order to approximate the objective Implicate Order is not unlike Eastern philosophies like Buddhism or Taoism, which were practiced long before the scientific method. An Indian philosopher once pointed out that the Hindus have known for 30,000 years that the Earth revolves around the sun, while the Europeans only realized this a few hundred years ago based on the work of Copernicus, Brahe, and Galileo. However, there is a fundamental difference between knowing via believing and knowing based on empirical research. A similar example is Aristotle's refusal to test whether a large stone would fall faster than a small one, as he knew the answer already 7 . Galileo eventually performed the experiment from the Leaning Tower in Pisa to demonstrate that the fall time of two objects is independent of their mass—which disproved Aristotle's theory of gravity that stipulated that objects fall at a speed proportional to their mass. Again, it demonstrates the power of empiricism and experimentation as formulated by Francis Bacon, John Locke, and others, over intuition and rationalizing.

Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data.

Following the evolution from the unicellular state to multicellular organisms—and reverse‐engineering it to a minimal‐cell state—reveals that biologic diversity is an artifact of the Explicate Order. Indeed, the unicell seems to be the primary level of selection in the Implicate Order, as it remains proximate to the First Principles of Physiology, namely negative entropy (negentropy), chemiosmosis, and homeostasis. The first two principles are necessary for growth and proliferation, whereas the last reflects Newton's Third Law of Motion that every action has an equal and opposite reaction so as to maintain homeostasis.

All organisms interact with their surroundings and assimilate their experience as epigenetic marks. Such marks extend to the DNA of germ cells and thus change the phenotypic expression of the offspring. The offspring, in turn, interacts with the environment in response to such epigenetic modifications, giving rise to the concept of the phenotype as an agent that actively and purposefully interacts with its environment in order to adapt and survive. This concept of phenotype based on agency linked to the Explicate Order fundamentally differs from its conventional description as a mere set of biologic characteristics. Organisms’ capacities to anticipate future stress situations from past memories are obvious in simple animals such as nematodes, as well as in plants and bacteria 8 , suggesting that the subjective Explicate Order controls both organismal behavior and trans‐generational evolution.

That perspective offers insight to the nature of consciousness: not as a “mind” that is separate from a “body”, but as an endogenization of physical matter, which complies with the Laws of Nature. In other words, consciousness is the physiologic manifestation of endogenized physical surroundings, compartmentalized, and made essential for all organisms by forming the basis for their physiology. Endocytosis and endocytic/synaptic vesicles contribute to endogenization of cellular surroundings, allowing eukaryotic organisms to gain knowledge about the environment. This is true not only for neurons in brains, but also for all eukaryotic cells 5 .

Such a view of consciousness offers insight to our awareness of our physical surroundings as the basis for self‐referential self‐organization. But this is predicated on our capacity to “experiment” with our environment. The burgeoning idea that we are entering the Anthropocene, a man‐made world founded on subjective senses instead of Natural Laws, is a dangerous step away from our innate evolutionary arc. Relying on just our senses and emotions, without experimentation and controls to understand the Implicate Order behind reality, is not just an abandonment of the principles of the Enlightenment, but also endangers the planet and its diversity of life.

Further reading

Anderson C (2008) The End of Theory: the data deluge makes the scientific method obsolete. Wired (December 23, 2008)

Bacon F (1620, 2011) Novum Organum Scientiarum. Nabu Press

Baluška F, Gagliano M, Witzany G (2018) Memory and Learning in Plants. Springer Nature

Charlesworth AG, Seroussi U, Claycomb JM (2019) Next‐Gen learning: the C. elegans approach. Cell 177: 1674–1676

Eliezer Y, Deshe N, Hoch L, Iwanir S, Pritz CO, Zaslaver A (2019) A memory circuit for coping with impending adversity. Curr Biol 29: 1573–1583

Gagliano M, Renton M, Depczynski M, Mancuso S (2014) Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia 175: 63–72

Gagliano M, Vyazovskiy VV, Borbély AA, Grimonprez M, Depczynski M (2016) Learning by association in plants. Sci Rep 6: 38427

Katz M, Shaham S (2019) Learning and memory: mind over matter in C. elegans . Curr Biol 29: R365‐R367

Kováč L (2007) Information and knowledge in biology – time for reappraisal. Plant Signal Behav 2: 65–73

Kováč L (2008) Bioenergetics – a key to brain and mind. Commun Integr Biol 1: 114–122

Koshland DE Jr (1980) Bacterial chemotaxis in relation to neurobiology. Annu Rev Neurosci 3: 43–75

Lyon P (2015) The cognitive cell: bacterial behavior reconsidered. Front Microbiol 6: 264

Margulis L (2001) The conscious cell. Ann NY Acad Sci 929: 55–70

Maximillian N (2018) The Metaphysics of Science and Aim‐Oriented Empiricism. Springer: New York

Mazzocchi F (2015) Could Big Data be the end of theory in science? EMBO Rep 16: 1250–1255

Moore RS, Kaletsky R, Murphy CT (2019) Piwi/PRG‐1 argonaute and TGF‐β mediate transgenerational learned pathogenic avoidance. Cell 177: 1827–1841

Peirce CS (1877) The Fixation of Belief. Popular Science Monthly 12: 1–15

Pigliucci M (2009) The end of theory in science? EMBO Rep 10: 534

Popper K (1959) The Logic of Scientific Discovery. Routledge: London

Posner R, Toker IA, Antonova O, Star E, Anava S, Azmon E, Hendricks M, Bracha S, Gingold H, Rechavi O (2019) Neuronal small RNAs control behavior transgenerationally. Cell 177: 1814–1826

Russell B (1912) The Problems of Philosophy. Henry Holt and Company: New York

Scerri E (2006) The Periodic Table: It's Story and Significance. Oxford University Press, Oxford

Shapiro JA (2007) Bacteria are small but not stupid: cognition, natural genetic engineering and socio‐bacteriology. Stud Hist Philos Biol Biomed Sci 38: 807–818

Torday JS, Miller WB Jr (2016) Biologic relativity: who is the observer and what is observed? Prog Biophys Mol Biol 121: 29–34

Torday JS, Rehan VK (2017) Evolution, the Logic of Biology. Wiley: Hoboken

Torday JS, Miller WB Jr (2016) Phenotype as agent for epigenetic inheritance. Biology (Basel) 5: 30

Wasserstein RL, Lazar NA (2016) The ASA's statement on p‐values: context, process and purpose. Am Statist 70: 129–133

Yamada T, Yang Y, Valnegri P, Juric I, Abnousi A, Markwalter KH, Guthrie AN, Godec A, Oldenborg A, Hu M, Holy TE, Bonni A (2019) Sensory experience remodels genome architecture in neural circuit to drive motor learning. Nature 569: 708–713

Ladislav Kováč discussed the advantages and drawbacks of the inductive method for science and the logic of scientific discoveries 9 . Obviously, technological advances have enabled scientists to expand the borders of knowledge, and informatics allows us to objectively analyze ever larger data‐sets. It was the telescope that enabled Tycho Brahe, Johannes Kepler, and Galileo Galilei to make accurate observations and infer the motion of the planets. The microscope provided Robert Koch and Louis Pasteur insights into the microbial world and determines the nature of infectious diseases. Particle colliders now give us a glimpse into the birth of the Universe, while DNA sequencing and bioinformatics have enormously advanced biology's goal to understand the molecular basis of life.

However, Kováč also reminds us that Bayesian inferences and reasoning have serious drawbacks, as documented in the instructive example of Bertrand Russell's “inductivist turkey”, which collected large amounts of reproducible data each morning about feeding time. Based on these observations, the turkey correctly predicted the feeding time for the next morning—until Christmas Eve when the turkey's throat was cut 9 . In order to avoid the fate of the “inductivist turkey”, mankind should also rely on Popperian deductive science, namely formulating theories, concepts, and hypotheses, which are either confirmed or refuted via stringent experimentation and proper controls. Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data. Moreover, before we start using our scientific instruments, we need to pose scientific questions. Therefore, as suggested by Albert Szent‐Györgyi, we need both Dionysian and Apollonian types of scientists 10 . Unfortunately, as was the case in Szent‐Györgyi's times, the Dionysians are still struggling to get proper support.

There have been pleas for reconciling philosophy and science, which parted ways owing to the rise of empiricism. This essay recognizes the centrality experiments and their controls for the advancement of scientific thought, and the attendant advance in philosophy needed to cope with many extant and emerging issues in science and society. We need a common “will” to do so. The rationale is provided herein, if only.

Acknowledgements

John Torday has been a recipient of NIH Grant HL055268. František Baluška is thankful to numerous colleagues for very stimulating discussions on topics analyzed in this article.

EMBO Reports (2019) 20 : e49110 [ Google Scholar ]

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John S Torday, Email: ude.alcu@yadrotj .

František Baluška, Email: ed.nnob-inu@aksulab .

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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What Are Constants & Controls of a Science Project Experiment?

What Is a Standardized Variable in Biology?

The scientific method involves asking a question, doing research, forming a hypothesis and testing the hypothesis via an experiment, so that the results can be analyzed. Every successful science experiment must include specific types of variables. There must be an independent variable, which changes throughout the course of an experiment; a dependent variable, which is observed and measured; and a controlled variable, also known as the "constant" variable, which must remain consistent and unchanging throughout the experiment. Even though the controlled or constant variable in an experiment does not change, it is every bit as important to the success of a science experiment as the other variables.

TL;DR (Too Long; Didn't Read)

TL;DR: In a science experiment, the controlled or constant variable is a variable that does not change. For example, in an experiment to test the effect of different lights on plants, other factors that affect plant growth and health, such as soil quality and watering, would need to remain constant.

Example of an Independent Variable

Let's say that a scientist is performing an experiment to test the effect of different lighting on houseplants. In this case, the lighting itself would be the independent variable, because it is the variable that the scientist is actively changing, over the course of the experiment. Whether the scientist is using different bulbs or altering the amount of light given to the plants, the light is the variable being altered, and is therefore the independent variable.

Example of a Dependent Variable

Dependent variables are the traits that a scientist observes, in relation to the independent variable. In other words, the dependent variable changes depending on the alterations made to the independent variable. In the houseplant experiment, the dependent variables would be the properties of the plants themselves, which the scientist is observing in relation to the changing light. These properties might include the plants' color, height and general health.

Example of a Controlled Variable

A controlled or constant variable does not change throughout the course of an experiment. It is vitally important that every scientific experiment include a controlled variable; otherwise, the conclusions of an experiment are impossible to understand. For example, in the houseplant experiment, controlled variables might be things such as the the quality of soil and the amount of water given to the plants. If these factors were not constant, and certain plants received more water or better soil than others, then there would be no way for the scientist to be sure that the plants weren't changing based on those factors instead of the different kinds of light. A plant might be healthy and green because of the amount of light it received, or it could be because it was given more water than the other plants. In this case, it would be impossible to draw proper conclusions based on the experiment.

However, if all plants are given the same amount of water and the same quality of soil, then the scientist can be sure that any changes from one plant to another are due to changes made to the independent variable: the light. Even though the controlled variable did not change and was not the variable actually being tested, it allowed the scientist to observe the cause-and-effect relationship between plant health and different types of lighting. In other words, it allowed for a successful scientific experiment.

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Maria Cook is a freelance and fiction writer from Indianapolis, Indiana. She holds an MFA in Creative Writing from Butler University in Indianapolis. She has written about science as it relates to eco-friendly practices, conservation and the environment for Green Matters.

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What Is a Control Group?

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A control group in a scientific experiment is a group separated from the rest of the experiment, where the independent variable being tested cannot influence the results. This isolates the independent variable 's effects on the experiment and can help rule out alternative explanations of the experimental results. Control groups can also be separated into two other types: positive or negative. Positive control groups are groups where the conditions of the experiment are set to guarantee a positive result. A positive control group can show the experiment is functioning properly as planned. Negative control groups are groups where the conditions of the experiment are set to cause a negative outcome. Control groups are not necessary for all scientific experiments. Controls are extremely useful where the experimental conditions are complex and difficult to isolate.

Example of a Negative Control Group

Negative control groups are particularly common in science fair experiments , to teach students how to identify the independent variable. A simple example of a control group can be seen in an experiment in which the researcher tests whether or not a new fertilizer has an effect on plant growth. The negative control group would be the set of plants grown without the fertilizer, but under the exact same conditions as the experimental group. The only difference between the experimental group would be whether or not the fertilizer was used.

There could be several experimental groups, differing in the concentration of fertilizer used, its method of application, etc. The null hypothesis would be that the fertilizer has no effect on plant growth. Then, if a difference is seen in the growth rate of the plants or the height of plants over time, a strong correlation between the fertilizer and growth would be established. Note the fertilizer could have a negative impact on growth rather than a positive impact. Or, for some reason, the plants might not grow at all. The negative control group helps establish that the experimental variable is the cause of atypical growth, rather than some other (possibly unforeseen) variable.

Example of a Positive Control Group

A positive control demonstrates an experiment is capable of producing a positive result. For example, let's say you are examining bacterial susceptibility to a drug. You might use a positive control to make sure the growth medium is capable of supporting any bacteria. You could culture bacteria known to carry the drug resistance marker, so they should be capable of surviving on a drug-treated medium. If these bacteria grow, you have a positive control that shows other drug-resistance bacteria should be capable of surviving the test.

The experiment could also include a negative control. You could plate bacteria known not to carry a drug resistance marker. These bacteria should be unable to grow on the drug-laced medium. If they do grow, you know there is a problem with the experiment.

• The Difference Between Control Group and Experimental Group
• Difference Between Independent and Dependent Variables
• Examples of Independent and Dependent Variables
• Phases of the Bacterial Growth Curve
• Null Hypothesis Examples
• What's the Difference Between Prejudice and Racism?
• Understanding Experimental Groups
• What Is the Difference Between a Control Variable and Control Group?
• What are Controlled Experiments?
• Understanding Simple vs Controlled Experiments
• The Role of a Controlled Variable in an Experiment
• Scientific Method Vocabulary Terms
• Scientific Variable
• What Is a Controlled Experiment?
• Dependent Variable Definition and Examples
• Independent Variable Definition and Examples

What is a Control in a Science Experiment?

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Importance of Controls in Science Experiments

You’ve been tasked to do a science experiment but you keep seeing reference to the word “control”. Just what is a control in a science experiment? By definition the control in a science experiment is a sample that remains the same throughout the experiment. The control must remain the same or equal at all times in order to receive accurate results. You can have as many controls as necessary to achieve results. For instance, when determining how far certain weights move based on wind velocity, the wind would be a control, staying the same, no matter what the weight. Controls are a vital part of a science experiment. If at any point, your variable could affect the end result of your experiment, it should be considered the control. Your control may change as your experiment changes. For instance, you may need a different sample to prove a different hypothesis.

How Does a Control Compare to Other Variables

When following the scientific method , you must have an independent and dependent variable. A control is just another type of variable. The three types of variables should not be confused as they are completely different. Independent variables are changes occurring due to the person doing the experiment. Dependent variables change based upon changes in the independent variables. Controlled variables are any other outside variables that may affect the dependent variable. The three variables can sometimes be easily mistaken. If you have not identified the control in a science experiment, you may be mistaking one of your controls as an independent variable. Remember that the control should never change. If your independent variable always remains the same, odds are it is your control.

How to Create Your Own Control Sample

Now we’ve covered what is a control in a science experiment, it’s time to see how it works in practice. Not all science experiments require a control, but many do. You can create your own control sample by following a few simple steps. One great example of creating a control in a relatively simple experiment is working with plants . The basis is to determine how plants grow in different types of soil mixtures. The control pot uses regular potting soil and the same daily routine of water and sun. The other pots have different soil mixtures and may be exposed to varying lights and temperatures. Depending on your science experiment, determine a variable or sample set that must remain the same at all times. The control may directly apply to every portion of your experiment, or it can be relative, such as the plant experiment. Another great example of creating a control is determining how fast an object sinks, or the object’s density. The control would be using the same amount of water in the exact same size container. Be sure to use the same type of water as well, such as filtered or unfiltered. Once the science experiment starts, document what your control is, along with your independent and dependent variables. This allows you to better monitor and keep track of your controlled variable. Controlled variables must be carefully set and monitored throughout your experiment. Any changes to the control will greatly alter your experiment’s results.

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On your drive home, ask Siri to turn on your lamps so the house is well-lit for your chicken dinner. That’s if you didn’t already have a schedule set to have them automatically turn on when the sun sets.

Or, get the full smart home experience by enabling geofencing within the Apple HomeKit app . This will allow your lights to automatically turn on when you’re within range of your home and shut off when you leave. 

Here are some more ideas of what you might automate with your new smart plugs:

• Air conditioners
• Holiday lights
• Bug zappers
• Electric grills

Really, anything that can automatically start once it’s plugged in will work with these plugs—think outside the box for how you can start automating your life and home.

Grab a 3-pack of the Belkin smart plugs for $69.99 (reg. $119.97) and save 41% , though they are also available in singles or 2-packs.

StackSocial prices subject to change.

Human Consciousness Is an Illusion, Scientists Say

The entire universe may have an internal mind—or the whole idea of consciousness could be a sham. Here’s why scientists still can’t agree.

The concept of panpsychism has been around for hundreds of years. Italian philosopher Francesco Patrizi coined the term in the late 16th century. He combined the Greek words παν (pronounced “pan,” and meaning everything) and ψυχή (psyche, meaning “soul” or “mind”) to describe a distinctive soulfulness inherent to each and every order of creation. The idea dates back even further, though, to ancient Greece, when astronomer, mathematician, and pre-Socratic philosopher Thales said “that everything is full of gods,” and one of the world’s best-studied philosophers, Plato, said that the world is indeed a living being endowed with a soul and intelligence.

In the 19th century, panpsychism took off in the West, championed by the likes of the great philosopher of pessimism, Arthur Schopenhauer, and the father of modern psychology, William James. Then came the philosophical movement that emerged in Vienna, Italy, in the 1920s, called logical positivism: the idea that scientific knowledge—empirically proven knowledge—was the only kind of acceptable knowledge, the rest being metaphysical mumbo-jumbo. It was game over for panpsychism.

Until recently.

The inability of empirical sciences to figure out why and how matter gives rise to the experiences of consciousness has recently rekindled an interest in panpsychism. So have developments in the fields of neuroscience, psychology, and quantum physics.

In 2004, Italian neuroscientist and psychiatrist Giulio Tononi, Ph.D., proposed the integrated information theory of consciousness, which says that consciousness is widespread and can be found even in some simple systems. In his article in Scientific American , leading American neuroscientist Christof Koch, Ph.D., bashed materialism and its view of emerging consciousness 10 years later. The notion of subjective feelings springing from physical stuff is at odds with a commonly applied axiom in philosophy and modern science: the “ ex nihilo nihil fit ,” or that “out of nothing, nothing comes, ” Koch wrote . He argued that elementary particles either have some charge, or they have none; similarly, where there are organized lumps of matter, consciousness follows.

.css-2l0eat{font-family:UnitedSans,UnitedSans-roboto,UnitedSans-local,Helvetica,Arial,Sans-serif;font-size:1.625rem;line-height:1.2;margin:0rem;padding:0.9rem 1rem 1rem;}@media(max-width: 48rem){.css-2l0eat{font-size:1.75rem;line-height:1;}}@media(min-width: 48rem){.css-2l0eat{font-size:1.875rem;line-height:1;}}@media(min-width: 64rem){.css-2l0eat{font-size:2.25rem;line-height:1;}}.css-2l0eat b,.css-2l0eat strong{font-family:inherit;font-weight:bold;}.css-2l0eat em,.css-2l0eat i{font-style:italic;font-family:inherit;} “Consciousness doesn’t exist, and we only think it does because we are under a sort of illusion about our own minds.”

Not everyone agrees though. Keith Frankish, Ph.D., a professor of philosophy at the University of Sheffield, believes today’s panpsychism is in a “metaphysical limbo,” a direct result of what he calls the “ depsychologization of consciousness .” This means that we try to grasp consciousness through what our senses perceive or through our immediate experiences, and that we refuse to acknowledge it as a psychological function. “The thought is that, if consciousness is not essentially connected to brain processes, then there’s no reason to think it must be restricted to brains . Maybe everything has a little inner glow to it, ” Frankish says. But it is exactly this view that tends to undermine the significance of consciousness. “If my consciousness makes no difference to how I react, why should I or anyone else care about it?” he asks. Frankish proposes a mirror image of panpsychism.

“Whereas panpsychists think that consciousness is everywhere, I think that consciousness—of the non-functional, inner glow kind—is nowhere,” he explains. “Consciousness doesn’t exist, and we only think it does because we are under a sort of illusion about our own minds, a view I call illusionism,” he continues. In other words, we humans have vastly extended the power of our biological brains and, through powerful tricks like self-manipulation or solid problem-solving skills, we have convinced ourselves we have a unified, conscious mind, a self, a soul—but it’s all an illusion, according to Frankish.

But illusionism is a view antithetical to what well-known biologist and author Rupert Sheldrake, Ph.D., believes. For Sheldrake, it’s an irrefutable fact that not only do we humans have consciousness, but the whole galaxy has consciousness, too. Sheldrake is best known for his hypothesis of morphic resonance, a process through which self-organizing systems (picture termite colonies or insulin molecules) inherit a memory from previous, similar systems. Similar organisms share mysterious telepathic interconnections, and species share collective memories: this is how your dog foretells when you’re coming home and why you feel awkward when someone is staring at you, according to Sheldrake. In a paper he published in the Journal of Consciousness Studies in 2021, Sheldrake asked “Is the sun conscious?” For him, it most certainly is.

“Consciousness does not need to be confined to brains,” Sheldrake says. “The link between minds and physical systems seems to be through rhythmic electromagnetic fields, which of course are present in our brains. They are also present in and around the sun, and these could be the interface between the solar mind and the body of the sun.” So, if the sun is conscious, it’s likely to be aware of activities within the solar system, continues Sheldrake, including here on Earth, and also of its relationship with other stars within the galaxy and the galaxy as a whole.

Perhaps it’s a matter of personal positioning in the world. Is nothing around me conscious? Is everything around me conscious? If the latter is true, where does my consciousness end and yours begin, and why is an intact brain conscious, whereas the same brain, pureed to goo inside a blender, is not ( as Koch ponders )? Will we ever know? No wonder scientists describe consciousness as the granddaddy of all mysteries of human behavior. If you subscribe to the panpsychist view, however, you may be startled to know that the conscious sun makes choices. “It may be able to choose in which direction to send out solar flares or coronal mass ejections, which can have an enormous effect on life on Earth, and to which our technologies are very vulnerable,” Sheldrake says.

Stav Dimitropoulos’s science writing has appeared online or in print for the BBC, Discover, Scientific American, Nature, Science, Runner’s World, The Daily Beast and others. Stav disrupted an athletic and academic career to become a journalist and get to know the world.

.css-cuqpxl:before{padding-right:0.3125rem;content:'//';display:inline;} Pop Mech Pro: Science .css-xtujxj:before{padding-left:0.3125rem;content:'//';display:inline;}

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Every Single Cell in Your Body Could Be Conscious

How to Live Forever, or Die Trying

A Groundbreaking Discovery For Interstellar Travel

Dark Matter Could Unlock a Limitless Energy Source

The Source of All Consciousness May Be Black Holes

Could Freezing Your Brain Help You Live Forever?

The Universe Could Be Eternal, This Theory Says

Immortality Is Impossible Until We Beat Physics

How Vacuum Energy Could Help Us Reach Light Speed

Centre for Behaviour Change

• Consultancy
• CBC Conference
• Related Organisations

Applying behavioural science to change gardening practices and support the transition to peat-free

Changing purchasing and gardening behaviours, to contribute towards policy-initiated peat-free horticulture targets.

1 July 2024

PhD project

Title:  Applying behavioural science to design strategies to change gardening practices and support the transition to peat-free

Funder:  Royal Horticultural Society  (RHS)

CBC Doctoral Student:  Zoe Upton

CBC Supervisors:   Dr Fabiana Lorencatto , Dr Lucy Porter

Start date:  2024

The RHS together with the Centre for Behaviour Change are on a mission to systematically design behaviour change interventions that support the transition to peat-free gardening practices, a crucial step towards a more sustainable future.

Why this research is important

Peat is the most widely used growing media due to its performance and low economic cost. However, peat extraction for horticultural use releases stored carbon, threatens biodiversity and increases flood risk. In recognition of the contribution of peat to the climate crisis, in March 2023 the UK government announced new policies and targets to restrict the use of peat-based compost over the coming years. One of the biggest challenges to address in the transition to peat-free will be behaviour change at different levels of the system, specifically industry, producers, and consumers. This PhD project focuses on the behaviour of consumers, in this case gardeners. The aim is to apply behavioural science frameworks to understand barriers and enablers to changing purchasing and gardening behaviours. We will co-design interventions to support consumers in changing their gardening practices and contribute towards the peat-free horticulture targets in line with new policies.

Find out more

Email Zoe:  [email protected]  

• Subscription

15 R Projects for Beginners (with Source Code)

R programming projects are essential for gaining practical data science experience. They provide the hands-on practice that bridges the gap between learning the required skills and deomonstrating you meet real-world job requirements. This process is particularly valuable when applying for jobs, as it addresses the common challenge of not having any experience when you're applying for your first data job .

A properly diversified portfolio of R projects will demonstrate your proficiency in:

• Data manipulation
• Data visualization
• Advanced statistical analysis

These skills are fundamental to making informed business decisions―so being able to demonstrate that you have them makes you a valuable asset to potential employers.

In this post, we'll explore 15 practical R project ideas. Each project is designed to highlight critical data science capabilities that will enhance your job prospects. Whether you're a student aiming to launch your career or a professional seeking advancement, these projects on R will show your ability to handle real-world data challenges effectively.

But first, to ensure you're developing in-demand R skills , we'll explain how to build your portfolio of projects on R by selecting the right ones and go over some of the common challenges you might face along the way. After we look at the 15 R project ideas in detail, we'll discuss how you can prepare for an R programming job.

Choosing the right R projects for your portfolio

Looking to improve your chances of landing a data science job? The R project ideas you select for your portfolio can make a big difference. A well-chosen set of projects on R shows off your skills and proves you can tackle real-world problems. Here's how to select R projects that help you grow, match your interests, and impress potential employers.

Find the sweet spot: Your skills, interests, and market demand

The best projects combine what you enjoy, what you're good at, and what employers want. This balance keeps you motivated and makes you more appealing to hiring managers. For example, if you love sports, you might create a project that uses R to predict game outcomes. This type of project lets you practice working with data and creating visualizations—skills that are valuable in many industries.

How to pick your R projects: A step-by-step approach

• Know your strengths (and weaknesses): Assess your R programming skills. What are you comfortable with? Where do you need practice? Knowing the answers these questions will help you choose projects that challenge you appropriately.
• Explore different tools and techniques: Pick projects that use various R packages and data types. This shows your versatility as a data scientist.
• Focus on solving problems: ChR project ideasoose projects with clear goals, like predicting customer behavior or analyzing social media trends. These projects are engaging and show employers you can deliver results.
• Seek feedback: Ask others to review your code and approach. Their input can help you improve your skills and projects.

Common challenges (and how to overcome them)

Many learners struggle with choosing projects on R that are too complex or aren't able to manage their time effectively. To avoid these issues:

• Start small : Begin with manageable projects that match your current skill level.
• Use available resources : When you get stuck, look for help in online tutorials or community forums .

Keep improving: The power of iteration

Don't stop after your first attempt. Reworking and refining your R projects based on feedback is key. This process of continuous improvement enhances the quality of your work and shows potential employers your commitment to excellence. It also helps prepare you for the workplace where iterating on your work is common.

Wrapping up

Carefully selecting your R project ideas can significantly improve your skills and how you present them to potential employers. As you review the list of 15 R project ideas below, use these tips to choose projects that will strengthen your portfolio and align with your career goals.

Getting started with R programming projects

Hands-on projects are key to developing practical R programming skills. They'll boost your understanding of the language and prepare you for real-world data tasks. Here's how to get started:

Common tools and packages

First, familiarize yourself with these R tools and packages:

• RStudio: An IDE that simplifies code writing, debugging, and visualization .
• dplyr : Streamlines data manipulation tasks .
• ggplot2 : Creates complex visualizations easily .
• data.table : Processes large datasets efficiently .

These tools will streamline your project workflow. For more insights, explore this guide on impactful R packages .

Setting up your project on R

Follow these steps to start your R programming project:

• Install R and RStudio: These are your foundational tools .
• Create a new project in RStudio: This keeps your files organized.
• Learn the RStudio environment: Understand each part of the IDE to get the most out of it .
• Import necessary packages: Load libraries like tidyverse or shiny as needed.

Overcoming common challenges

As a beginner, you might face some hurdles. Here are some strategies to help:

• Keep your code organized and use Git for version control.
• Start small to build confidence before tackling complex projects.
• Use community forums and official documentation when you need help.

15 R Project Ideas with Source Code

The beauty of the following R projects lies in their diverse range of scenarios. You'll start by investigating COVID-19 virus trends and soon find yourself analyzing forest fire data. This variety ensures that you can apply your R programming skills to uncover valuable insights in different contexts. Although most of these R projects are suitable for beginners, the more advanced ones towards the end of the list may require additional effort and expertise to complete.

Here's what we'll cover:

Beginner R Projects

• Investigating COVID-19 Virus Trends
• Creating An Efficient Data Analysis Workflow
• Creating An Efficient Data Analysis Workflow, Part 2
• Analyzing Forest Fire Data
• NYC Schools Perceptions
• Analyzing Movie Ratings

Intermediate R Projects

• New York Solar Resource Data
• Investigating Fandango Movie Ratings
• Finding the Best Markets to Advertise In
• Mobile App for Lottery Addiction
• Building a Spam Filter with Naive Bayes
• Winning Jeopardy

Advanced R Projects

• Predicting Condominium Sale Prices
• Predicting Car Prices
• Creating a Project Portfolio

In the sections that follow, we'll provide detailed walkthroughs for each project. You'll find step-by-step instructions and expected outcomes to guide you through the process. Let's get started with building your portfolio of projects on R!

1. Investigating COVID-19 Virus Trends

Difficulty Level: Beginner

In this beginner-level R project, you'll step into the role of a data analyst exploring the global COVID-19 pandemic using real-world data. Leveraging R and the powerful dplyr library, you'll manipulate, filter, and aggregate a comprehensive dataset containing information on COVID-19 cases, tests, and hospitalizations across different countries. By applying data wrangling techniques such as grouping and summarizing, you'll uncover which countries have the highest rates of positive COVID-19 tests relative to their testing numbers. This hands-on project will not only strengthen your R programming skills and analytical thinking but also provide valuable experience in deriving actionable insights from real-world health data – a crucial skill in today's data-driven healthcare landscape.

Tools and Technologies

Prerequisites.

To successfully complete this project, you should be comfortable with data structures in R such as:

• Creating and working with vectors, matrices, and lists in R
• Indexing data structures to extract elements for analysis
• Applying functions to data structures to perform calculations
• Manipulating and analyzing data using dataframes

Step-by-Step Instructions

• Load and explore the COVID-19 dataset using readr and tibble
• Filter and select relevant data using dplyr functions
• Aggregate data by country and calculate summary statistics
• Identify top countries by testing numbers and positive case ratios
• Create vectors and matrices to store key findings
• Compile results into a comprehensive list structure

Expected Outcomes

Upon completing this project, you'll have gained valuable skills and experience, including:

• Analyzing a real-world COVID-19 dataset using R and dplyr
• Applying data manipulation techniques to filter and aggregate data
• Identifying trends and insights from data using grouping and summarizing
• Creating and manipulating different R data structures (vectors, matrices, lists)
• Interpreting results to answer specific questions about COVID-19 testing and positive rates

Relevant Links and Resources

• R Project Example Solution
• Original COVID19 Worldwide Testing dataset on Kaggle

Additional Resources

• WHO Coronavirus (COVID-19) Dashboard

2. Creating An Efficient Data Analysis Workflow

In this hands-on, beginner-level project with R, you'll step into the role of a data analyst for a company selling programming books. Using R and RStudio, you'll analyze their sales data to determine which titles are most profitable. By applying key R programming concepts like control flow, loops, and functions, you'll develop an efficient data analysis workflow. This project provides valuable practice in data cleaning, transformation, and analysis, culminating in a structured report of your findings and recommendations.

To successfully complete this project, you should be comfortable with control flow, iteration, and functions in R including:

• Implementing control flow using if-else statements
• Employing for loops and while loops for iteration
• Writing custom functions to modularize code
• Combining control flow, loops, and functions in R
• Load and explore the book sales dataset using tidyverse
• Clean the data by handling missing values and inconsistent labels
• Transform the review data into numerical format
• Analyze the cleaned data to identify top-performing titles
• Summarize findings and provide data-driven recommendations
• Applying R programming concepts to real-world data analysis
• Developing an efficient, reproducible data analysis workflow
• Cleaning and preparing messy data for analysis using tidyverse
• Analyzing sales data to derive actionable business insights
• Communicating findings and recommendations to stakeholders
• Getting Started with R and RStudio - Dataquest Blog

In this beginner-level R project, you'll step into the role of a data analyst at a book company tasked with evaluating the impact of a new program launched on July 1, 2019 to encourage customers to buy more books. Using R and powerful packages like dplyr, stringr, and lubridate, you'll clean and analyze the company's 2019 sales data to determine if the program successfully boosted book purchases and improved review quality. You'll handle missing data, process text reviews, and compare key metrics before and after the program launch. This project offers hands-on experience in applying data manipulation techniques to real-world business data, strengthening your skills in efficient data analysis and deriving actionable insights.

• tidyverse (including dplyr)

To successfully complete this project, you should be comfortable with specialized data processing techniques in R , including:

• Manipulating strings using stringr functions
• Working with dates and times using lubridate
• Applying the map function to vectorize custom functions
• Understanding and employing regular expressions for pattern matching
• Load and explore the book company's 2019 sales data
• Clean the data by handling missing values and inconsistencies
• Process text reviews to determine positive/negative sentiment
• Compare key sales metrics before and after the program launch date
• Analyze differences in sales between customer segments
• Evaluate changes in review sentiment and summarize findings
• Cleaning and preparing a real-world business dataset for analysis using R
• Applying powerful R packages to manipulate and process data efficiently
• Analyzing sales data to quantify the impact of a new business initiative
• Translating data analysis findings into meaningful business insights
• Project Dataset

4. Analyzing Forest Fire Data

In this beginner-level data analysis project in R, you'll analyze a dataset on forest fires in Portugal to uncover patterns in fire occurrence and severity. Using R and powerful data visualization techniques, you'll explore factors such as temperature, humidity, and wind speed to understand their relationship with fire spread. You'll create engaging visualizations, including bar charts, box plots, and scatter plots, to reveal trends over time and across different variables. By completing this project, you'll gain valuable insights into the ecological impact of forest fires while strengthening your skills in data manipulation, exploratory data analysis, and creating meaningful visualizations using R and ggplot2.

• tidyverse (including ggplot2)

To successfully complete this project, you should be comfortable with data visualization techniques in R and have experience with:

• Working with variables, data types, and data structures in R
• Importing and manipulating data using R data frames
• Creating basic plots using ggplot2 (e.g., bar charts, scatter plots)
• Transforming and preparing data for visualization
• Load and explore the forest fires dataset using R and tidyverse
• Process the data, converting relevant columns to appropriate data types (e.g., factors for month and day)
• Create bar charts to analyze fire occurrence patterns by month and day of the week
• Use box plots to explore relationships between environmental factors and fire severity
• Implement scatter plots to investigate potential outliers and their impact on the analysis
• Summarize findings and discuss implications for forest fire prevention strategies
• Cleaning and preparing real-world ecological data for analysis using R
• Creating various types of plots (bar charts, box plots, scatter plots) using ggplot2
• Interpreting visualizations to identify trends in forest fire occurrence and severity
• Handling outliers and understanding their impact on data analysis and visualization
• Communicating data-driven insights for environmental decision-making
• UCI Machine Learning Repository: Forest Fires Dataset

5. NYC Schools Perceptions

In this beginner-level R project, you'll explore real-world survey data on school quality perceptions in New York City. Using R and various data manipulation packages, you'll clean, reshape, and visualize responses from students, parents, and teachers to uncover insights about school performance. You'll work with a large, complex dataset to build valuable data wrangling and exploration skills while creating an impactful analysis of NYC school quality perceptions across different stakeholder groups.

• R Notebooks
• tidyverse (dplyr, tidyr, ggplot2)

To successfully complete this project, you should be comfortable with data cleaning techniques in R including:

• Manipulating DataFrames using dplyr
• Joining and combining relational data
• Handling missing data through various techniques
• Reshaping data between wide and long formats using tidyr
• Creating visualizations with ggplot2
• Load and clean the NYC school survey datasets
• Join survey data with school performance data
• Create a correlation matrix to identify relationships between variables
• Visualize strong correlations using scatter plots
• Reshape the data to compare perceptions across stakeholder groups
• Analyze and visualize differences in perceptions using box plots
• Cleaning and wrangling complex, real-world datasets using tidyverse tools
• Joining multiple datasets to create a comprehensive analysis
• Identifying correlations and visualizing relationships in data
• Reshaping data to facilitate comparisons across different groups
• Creating informative visualizations to communicate insights about school quality perceptions
• Interpreting results to draw meaningful conclusions about NYC schools
• NYC School Survey Data on NYC Open Data

6. Analyzing Movie Ratings

In this beginner-level project with R, you'll analyze movie ratings data from IMDb using web scraping techniques in R. You'll extract information such as titles, release years, runtimes, genres, ratings, and vote counts for the top 30 movies released between March and July 2020. Using packages like rvest and dplyr, you'll practice loading web pages, identifying CSS selectors, and extracting specific data elements. You'll also gain experience in data cleaning by handling missing values. Finally, you'll use ggplot2 to visualize the relationship between user ratings and number of votes, uncovering trends in movie popularity and reception. This project offers hands-on experience in web scraping, data manipulation, and visualization using R, skills that are highly valuable in real-world data analysis scenarios.

To successfully complete this project, you should be familiar with web scraping techniques in R and have experience with:

• Understanding HTML structure and using CSS selectors to locate specific elements
• Using the rvest package to extract data from web pages
• Basic data manipulation and cleaning using dplyr and stringr
• Working with vectors and data frames in R
• Load the IMDb web page and extract movie titles and release years
• Extract additional movie features such as runtimes and genres
• Scrape user ratings, metascores, and vote counts for each movie
• Clean the extracted data and handle missing values
• Create a data frame combi ning all extracted information
• Visualize the relationship between user ratings and vote counts using ggplot2
• Implementing web scraping techniques to extract structured data from IMDb
• Cleaning and preprocessing scraped data for analysis
• Creating a comprehensive dataset of movie information from multiple web elements
• Visualizing relationships between movie ratings and popularity
• Applying R programming skills to solve real-world data extraction and analysis problems
• IMDb Top 30 Movies (March-July 2020)

7. New York Solar Resource Data

Difficulty Level: Intermediate

In this beginner-friendly R project, you'll step into the role of a data analyst tasked with extracting solar resource data for New York City using the Data Gov API. Using R, you'll apply your skills in API querying, JSON parsing, and data structure manipulation to retrieve the data and convert it into a format suitable for analysis. This project provides hands-on experience in working with real-world data from web APIs, a crucial skill for data scientists working with diverse data sources.

To successfully complete this project, you should be comfortable with working with APIs in R and have experience with:

• Making API requests using the httr package
• Parsing JSON responses with jsonlite
• Manipulating data frames using dplyr
• Creating basic visualizations with ggplot2
• Working with complex list structures in R
• Set up the API request parameters and make a GET request to the NREL API
• Parse the JSON response and extract relevant data into R objects
• Convert the extracted data into a structured dataframe
• Create a custom function to streamline the data extraction process
• Visualize the solar resource data using ggplot2
• Extracting data from web APIs using R and the httr package
• Parsing and manipulating complex JSON data structures
• Creating custom functions to automate data retrieval and processing
• Visualizing time-series data related to solar resources
• Applying data wrangling techniques to prepare API data for analysis
• NREL Solar Resource Data API Documentation
• Data.gov - Open Data Source

8. Investigating Fandango Movie Ratings

In this beginner-friendly project with R, you'll investigate potential bias in Fandango's movie rating system. A 2015 analysis revealed that Fandango's ratings were inflated. Your task is to compare movie ratings data from 2015 and 2016 to determine if Fandango's system changed after the bias was exposed. Using R and statistical analysis techniques, you'll explore rating distributions, calculate summary statistics, and visualize changes in rating patterns. This project provides hands-on experience with a real-world data integrity investigation, strengthening your skills in data manipulation, statistical analysis, and data visualization.

To successfully complete this project, you should be familiar with fundamental statistics concepts in R and have experience with:

• Data manipulation using dplyr (filtering, selecting, mutating, summarizing)
• Working with string data using stringr functions
• Reshaping data with tidyr (gather, spread)
• Calculating summary statistics (mean, median, mode)
• Creating and customizing plots with ggplot2 (density plots, bar plots)
• Interpreting frequency distributions and probability density functions
• Basic hypothesis testing and statistical inference
• Load and explore the 2015 and 2016 Fandango movie ratings datasets
• Clean and preprocess the data, isolating relevant samples for analysis
• Compare distribution shapes of 2015 and 2016 ratings using kernel density plots
• Calculate and compare summary statistics for both years
• Visualize changes in rating patterns using bar plots
• Interpret results and draw conclusions about changes in Fandango's rating system
• Conducting a comparative analysis of rating distributions using R
• Applying statistical techniques to investigate potential bias in ratings
• Creating informative visualizations to illustrate changes in rating patterns
• Drawing and communicating data-driven conclusions about rating system integrity
• Implementing end-to-end data analysis workflow in R, from data loading to insight generation
• Original Fandango Ratings Dataset
• Original FiveThirtyEight Article on Fandango Ratings

9. Finding the Best Markets to Advertise In

In this beginner-friendly R project, you'll step into the role of an analyst for an e-learning company offering programming courses. Your task is to analyze survey data from freeCodeCamp to determine the two best markets for advertising your company's products. Using R, you'll explore factors such as new coder locations, market densities, and willingness to pay for learning. By applying statistical concepts and data analysis techniques, you'll provide actionable insights to optimize your company's advertising strategy and drive growth.

To successfully complete this project, you should be comfortable with intermediate statistics concepts in R such as:

• Summarizing distributions using measures of central tendency
• Calculating variance and standard deviation
• Standardizing values using z-scores
• Locating specific values in distributions using z-scores
• Load and explore the freeCodeCamp survey data
• Analyze the locations and densities of new coders in different markets
• Calculate and compare average monthly spending on learning across countries
• Identify and handle outliers in the spending data
• Determine the two best markets based on audience size and willingness to pay
• Summarize findings and make recommendations for the advertising strategy
• Applying statistical concepts to inform strategic business decisions
• Using R to analyze real-world survey data and derive actionable insights
• Handling outliers and cleaning data for more accurate analysis
• Translating data analysis results into clear recommendations for stakeholders
• Developing a data-driven approach to optimizing marketing strategies
• The 2017 freeCodeCamp New Coder Survey Data
• freeCodeCamp's New Coder Survey Results

10. Mobile App for Lottery Addiction

In this beginner-friendly data science project in R, you'll develop the logical core of a mobile app designed to help lottery addicts understand their chances of winning. As a data analyst at a medical institute, you'll use R programming, probability theory, and combinatorics to analyze historical data from the Canadian 6/49 lottery. You'll create functions to calculate various winning probabilities, check for previous winning combinations, and provide users with a realistic view of their odds. This project offers hands-on experience in applying statistical concepts to a real-world problem while building your R programming portfolio.

• tidyverse package
• sets package

To successfully complete this project, you should be comfortable with fundamental probability concepts in R such as:

• Calculating theoretical and empirical probabilities
• Applying basic probability rules
• Working with permutations and combinations
• Using R functions for complex probability calculations
• Manipulating data with tidyverse packages
• Implement core probability functions for lottery calculations
• Calculate the probability of winning the jackpot with a single ticket
• Analyze historical lottery data to check for previous winning combinations
• Develop functions to calculate probabilities for multiple tickets and partial matches
• Create user-friendly outputs to communicate lottery odds effectively
• Applying probability and combinatorics concepts to a real-world scenario
• Implementing complex probability calculations using R functions
• Working with historical data to inform statistical analysis
• Developing logical components for a mobile application
• Communicating statistical concepts to a non-technical audience
• 6/49 Lottery Dataset on Kaggle

11. Building a Spam Filter with Naive Bayes

In this beginner-friendly project with R, you'll build an SMS spam filter using the Naive Bayes algorithm. Working with a dataset of labeled SMS messages, you'll apply text preprocessing techniques, implement the Naive Bayes classifier from scratch, and evaluate its performance. This project offers hands-on experience in applying probability theory to a real-world text classification problem, providing valuable skills for aspiring data scientists in natural language processing and spam detection. You'll gain practical experience in data preparation, probability calculations, and implementing machine learning algorithms in R.

• Naive Bayes algorithm

To successfully complete this project, you should be familiar with conditional probability concepts in R and have experience with:

• Basic R programming and data manipulation using tidyverse
• Understanding and applying conditional probability rules
• Calculating probabilities based on prior knowledge using Bayes' theorem
• Text preprocessing techniques in R
• Load and preprocess the SMS dataset, creating training, cross-validation, and test sets
• Clean the text data and build a vocabulary from the training set
• Calculate probability parameters for the Naive Bayes classifier
• Implement the Naive Bayes algorithm to classify new messages
• Evaluate the model's performance and tune hyperparameters using cross-validation
• Test the final model on the test set and interpret results
• Implementing text preprocessing techniques for machine learning tasks
• Building a Naive Bayes classifier from scratch in R
• Applying probability calculations in a real-world text classification problem
• Evaluating and optimizing machine learning model performance
• Interpreting classification results in the context of spam detection
• UCI Machine Learning Repository: SMS Spam Collection Dataset

12. Winning Jeopardy

In this beginner-friendly R project, you'll analyze a dataset of over 20,000 Jeopardy questions to uncover patterns that could give you an edge in the game. Using R and statistical techniques, you'll explore question categories, identify terms associated with high-value clues, and develop data-driven strategies to improve your odds of winning. You'll apply chi-squared tests and text analysis methods to determine which categories appear most frequently and which topics are associated with higher-value questions. This project will strengthen your skills in hypothesis testing, string manipulation, and deriving actionable insights from text data.

• Chi-squared test

To successfully complete this project, you should be familiar with hypothesis testing in R and have experience with:

• Performing chi-squared tests on categorical data
• Manipulating strings and text data in R
• Data cleaning and preprocessing techniques
• Basic data visualization in R
• Load and preprocess the Jeopardy dataset, cleaning text and converting data types
• Normalize dates to make them more accessible for analysis
• Analyze the frequency of question categories using chi-squared tests
• Identify unique terms in questions and associate them with question values
• Perform statistical tests to determine which terms are associated with high-value questions
• Visualize and interpret the results to develop game strategies
• Applying chi-squared tests to analyze categorical data in a real-world context
• Implementing text preprocessing and analysis techniques in R
• Interpreting statistical results to derive actionable insights
• Developing data-driven strategies for game show success
• Original Jeopardy Dataset

13. Predicting Condominium Sale Prices

Difficulty Level: Advanced

In this challenging project with R, you'll analyze New York City condominium sales data to predict prices based on property size. Using R and linear regression modeling techniques, you'll clean and explore the dataset, visualize relationships between variables, and build predictive models. You'll compare model performance across NYC's five boroughs (Manhattan, Brooklyn, Queens, The Bronx, and Staten Island), gaining valuable experience in real estate data analysis and statistical modeling. This project will strengthen your skills in data cleaning, exploratory analysis, and interpreting regression results in a practical business context.

• Linear regression

To successfully complete this project, you should be familiar with linear regression modeling in R and have experience with:

• Data manipulation and cleaning using tidyverse functions
• Creating scatterplots and other visualizations with ggplot2
• Fitting and interpreting linear regression models in R
• Evaluating model performance using metrics like R-squared and RMSE
• Basic understanding of real estate market dynamics
• Load and clean the NYC condominium sales dataset
• Perform exploratory data analysis, visualizing relationships between property size and sale price
• Identify and handle outliers that may impact model performance
• Build a linear regression model for all NYC boroughs combined
• Create separate models for each borough and compare their performance
• Interpret results and draw conclusions about price prediction across different areas of NYC
• Cleaning and preparing real estate data for analysis in R
• Visualizing and interpreting relationships between property features and prices
• Building and comparing linear regression models across different market segments
• Evaluating model performance and understanding limitations in real estate price prediction
• Translating statistical results into actionable insights for real estate analysis
• R-bloggers: A great resource for R programming tips and tutorials

14. Predicting Car Prices

In this challenging R project, you'll step into the role of a data scientist tasked with developing a model to predict car prices for a leading automotive company. Using a dataset of various car attributes such as make, fuel type, body style, and engine specifications, you'll apply the k-nearest neighbors algorithm in R to build an optimized prediction model. You'll go through the complete machine learning workflow - from data exploration and preprocessing to model evaluation and interpretation. This project will strengthen your skills in examining relationships between predictors, implementing cross-validation, performing hyperparameter optimization, and comparing different models to create an effective price prediction tool that could be used in real-world automotive market analysis.

• caret package
• k-nearest neighbors algorithm

To successfully complete this project, you should be comfortable with fundamental machine learning concepts in R such as:

• Understanding the key steps in a typical machine learning workflow
• Implementing k-nearest neighbors for regression tasks
• Using the caret library for machine learning model training and evaluation in R
• Evaluating model performance using error metrics (e.g., RMSE) and k-fold cross validation
• Basic data manipulation and visualization using dplyr and ggplot2
• Load and preprocess the car features and prices dataset, handling missing values and non-numerical columns
• Explore relationships between variables using feature plots and identify potential predictors
• Prepare training and test sets by splitting the data using createDataPartition
• Implement k-nearest neighbors models using caret, experimenting with different values of k
• Conduct 5-fold cross-validation and hyperparameter tuning to optimize model performance
• Evaluate the final model on the test set, interpret results, and discuss potential improvements
• Applying the end-to-end machine learning workflow in R to a real-world prediction problem
• Implementing and optimizing k-nearest neighbors models for regression tasks using caret
• Using resampling techniques like k-fold cross validation for robust model evaluation
• Interpreting model performance metrics (e.g., RMSE) in the context of car price prediction
• Gaining practical experience in feature selection, preprocessing, and hyperparameter tuning
• Developing intuition for model selection and performance optimization in regression tasks
• Original Automobile Dataset on UCI Machine Learning Repository

15. Creating a Project Portfolio

In this challenging project with R, you'll be tasked with creating an impressive interactive portfolio to showcase your R programming and data analysis skills to potential employers. Using Shiny, you'll compile your guided projects from Dataquest R courses into one cohesive portfolio app. You'll apply your Shiny skills to incorporate R Markdown files, customize your app's appearance, and deploy it for easy sharing. This project will strengthen your ability to create interactive web applications, integrate multiple data projects, and effectively present your work to enhance your job prospects in the data analysis field.

To successfully complete this project, you should be comfortable with building interactive web applications in Shiny and have experience with:

• Understanding the structure and components of a Shiny app
• Creating inputs and outputs in the Shiny user interface
• Programming the server logic to connect inputs and outputs
• Extending Shiny apps with additional features
• Basic R Markdown usage for creating dynamic reports
• Plan the structure and content of your portfolio app
• Build the user interface with a navigation bar and project pages
• Incorporate R Markdown files for individual project showcases
• Develop server logic to handle user interactions and display content
• Create a utility function to efficiently generate project pages
• Design an engaging splash page and interactive resume section
• Deploy your portfolio app to shinyapps.io for easy sharing
• Building a comprehensive, interactive portfolio app using Shiny
• Integrating multiple R projects and analyses into a cohesive presentation
• Creating utility functions to streamline app development
• Customizing Shiny app appearance and functionality for professional presentation
• Deploying a Shiny app to a public hosting platform for easy access
• Effectively showcasing your R programming and data analysis skills to potential employers
• Resolved R Shiny app issue regarding images in the Dataquest Community
• Non-Guided Project: Making an R Shiny App to track moths | Dataquest Community

How to Prepare for an R Programming Job

Looking to land your first R programming job? Let's walk through the key steps to prepare yourself for success in this field.

Understand Market Demands

Start by researching what employers want. Browse R programming job listings on popular job listing sites like the ones below. They'll give you a clear picture of the skills and qualifications currently in demand.

Once you have a good idea of the skills employers are looking for, take on projects that help you develop and demonstrate those in-demand skills.

Develop Essential Skills

For entry-level positions, focus on being able to demonstrate these skills:

• Data manipulation (using packages like dplyr )
• Data analysis and visualization (with tools like ggplot2 )
• Basic statistical analysis
• Fundamental machine learning concepts
• Core programming principles

To build these skills:

• Enroll in structured learning paths or bootcamps
• Work on hands-on coding projects
• Participate in coding competitions to enhance problem-solving skills

As you learn, you might find some concepts challenging. Don't get discouraged. Instead:

• Practice coding regularly to improve your speed and accuracy
• Seek feedback from peers or mentors to refine your code quality and problem-solving approach

Showcase Your Work

Create a portfolio that highlights your R projects. Include examples demonstrating your data analysis, visualization, and statistical computing skills. Consider using GitHub to host your work , ensuring each project is well-documented.

Prepare for the Job Hunt

Tailor your resume to emphasize relevant technical skills and project experiences. For interviews, be ready to discuss your projects in detail . Practice explaining how you've applied specific R functions and packages to solve real-world problems.

Remember, becoming job-ready in R programming is a journey that combines technical skill development, practical experience, and effective self-presentation. By following these steps and persistently honing your skills, you'll be well-equipped to pursue opportunities in the data science field using R.

Bottom line: R programming projects are essential for building real-world skills and advancing your data science career. Here's why they matter and how to get started:

• Practical application : Projects help you apply theory to actual problems.
• Career advancement : They showcase your abilities to potential employers.
• Skill development : Start simple and gradually tackle more complex challenges.

If you're new to R, begin with basic projects focusing on data cleaning and visualization. This approach builds your confidence and expertise gradually. As you progress, adopt good coding practices. Clear, well-organized code is easier to read and maintain, especially when collaborating with others.

Consider exploring Dataquest's Data Analyst in R path . This program covers everything from basic concepts to advanced data techniques.

R projects do more than beef up your portfolio. They sharpen your problem-solving skills and prepare you for real data science challenges. Start with a project that interests you and matches your current skills. Then, step by step, move to more complex problems. Let your interest in data guide your learning journey.

Remember, every R project you complete brings you closer to your data science goals. So, pick a project and start coding!

More learning resources

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