does every science experiment need a control

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Biology archive

Course: biology archive > unit 1.

The scientific method

Controlled experiments

The scientific method and experimental design

Introduction

How are hypotheses tested.

One pot of seeds gets watered every afternoon.
The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍ and coral bleaching.

What your control and experimental groups would be
What your independent and dependent variables would be
What results you would predict in each group

Experimental setup

Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍ around 8.2 ‍ ). The corals in these tanks served as the control group .
Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍ . One set of tanks was medium-acidity ( pH ‍ about 7.9 ‍ ), while another set was high-acidity ( pH ‍ about 7.65 ‍ ). Both the medium-acidity and high-acidity groups were experimental groups .
In this experiment, the independent variable was the acidity ( pH ‍ ) of the seawater. The dependent variable was the degree of bleaching of the corals.
The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍ fragments of coral, and there were 5 ‍ identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍ values were all above the neutral pH ‍ of 7.0 ‍ . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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v.20(10); 2019 Oct 4

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.

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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.

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 ]

Contributor Information

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

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

Controlled Experiment

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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.

controlled experiment extraneous variables

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.

Research Methodology

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Methodology
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)

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

Prevent plagiarism, run a free check.

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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Bhandari, P. (2022, October 10). Controlled Experiments | Methods & Examples of Control. Scribbr. Retrieved 18 June 2024, from https://www.scribbr.co.uk/research-methods/controlled-experiments/

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Pritha Bhandari

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Understanding Experimental Controls

Experimentation

Much of the training that scientists receive in graduate school is experiential, you learn how to do an experiment by working in a laboratory and performing experiments. In my opinion, not enough time and effort is devoted to understanding the philosophy and methods of experimental design.

An experiment without the proper controls is meaningless. Controls allow the experimenter to minimize the effects of factors other than the one being tested. It’s how we know an experiment is testing the thing it claims to be testing.

This goes beyond science — controls are necessary for any sort of experimental testing, no matter the subject area. This is often why so many bibliometric studies of the research literature are so problematic. Inadequate controls are often performed which fail to eliminate the effects of confounding factors, leaving the causality of any effect seen to be undetermined.

Novartis’ David Glass has put together the videos below, showing some of the basics of experimental validation and controls (Full disclosure: I was an editor on the first edition of David’s book on experimental design). These short videos offer quick lessons in positive and negative controls, as well as how to validate your experimental system.

These are great starting points, and I highly recommend Glass’ book, now in its second edition , if you want to dig deeper and understand the nuances of the different types of negative and positive controls, not to mention method and reagent controls, subject controls, assumption controls and experimentalist controls.

David Crotty

David Crotty is a Senior Consultant at Clarke & Esposito, a boutique management consulting firm focused on strategic issues related to professional and academic publishing and information services. Previously, David was the Editorial Director, Journals Policy for Oxford University Press. He oversaw journal policy across OUP’s journals program, drove technological innovation, and served as an information officer. David acquired and managed a suite of research society-owned journals with OUP, and before that was the Executive Editor for Cold Spring Harbor Laboratory Press, where he created and edited new science books and journals, along with serving as a journal Editor-in-Chief. He has served on the Board of Directors for the STM Association, the Society for Scholarly Publishing and CHOR, Inc., as well as The AAP-PSP Executive Council. David received his PhD in Genetics from Columbia University and did developmental neuroscience research at Caltech before moving from the bench to publishing.

7 Thoughts on "Understanding Experimental Controls"

We could add one more necessary control in this experiment–controlling for variability in individual response.

In the three videos, the experimenter may only detect differences between groups (or average differences). He is unable to detect changes in individuals. Some participants may be more sensitive to caffeine than others, some may show negative changes, and some may show no changes at all. If we take the blood pressure of participants before they drink coffee, we have a baseline measurement for all individuals. We also have a check on whether the experimenter was able to randomly assign participants to each treatment group.

In effect, each individual is their own control, with a before and after measurement. The experimenter is looking at the change in response of the individual rather than the average effect of the group. It is a much more sensitive way to structure and analyze experiments like this.

By Phil Davis
Nov 2, 2018, 8:57 AM

Agreed, these videos only skim the surface (his book goes into much greater detail about a much wider range of controls).

By David Crotty
Nov 2, 2018, 9:05 AM

Most experimenters who use random assignment to control and treatment groups have found that post-test only design works as well as pre-/post-test design.

Nov 2, 2018, 10:01 AM

I don’t see how. By controlling for a potentially large source of variability—the individual participant—statistical tests become much more sensitive to changes than averaging all of that variability by group in a simple post-test design. Second, it is a check to see whether the randomization of participants into groups was successful. In many RTCs in the clinical sciences, there is recruitment bias, allowing for the sicker patients to be placed in the treatment group, for example.

Nov 2, 2018, 12:55 PM

No mention of Institutional Review Board?! The IRB will raise Dr. Johnson’s own blood pressure.

And then there’s the issue of Dr. Johnson’s White Coat — that might trigger considerable individual variation. (My own blood pressure readings change markedly in the course of a visit to the doctor. )

Nov 2, 2018, 4:59 PM

I believe that IRB approval is discussed in the video on system validation.

Nov 2, 2018, 5:02 PM

Late to the debate, but I think those are wonderful. Maybe next Control Kitty will ask just how he assembled all those volunteers for his test to be representative and blinding to minimize bias. Were they self-selected? A bunch of caffeine habituated javaheads who responded to an ad in the coffee shop? I could see another video on randomization and sampling frames. I’m sure David Glass’s book goes into all that, but well, I have a shelf full of related books and I’m unlikely to benefit from and want to buy another. Unless maybe he hooks with another clever video or two. Go Kitty! Except, ~900 views! That’s sad. I might have sneak in citations to them. (I tend to get chastised by reviewers/editors for citing non-scholarly sources.) Something like this might slip under the editor’s radar: Glass, D. 2018. Experimental Design for Biologists: 1. System Validation. Video (4:06 minutes). YouTube. https://www.youtube.com/watch?v=qK9fXYDs–8 [Accessed November 11, 2018].

By Chris Mebane
Nov 12, 2018, 12:17 AM

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant in an experiment.

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

They make it easier to reproduce the experiment.
The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

Duration of the experiment
Size and composition of containers
Temperature
Sample volume
Experimental technique
Chemical purity or manufacturer
Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

does every science experiment need a control

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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What Is a Controlled Experiment?

Definition and Example

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Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
B.A., Physics and Mathematics, Hastings College

A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

Box, George E. P., et al. Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005.
Creswell, John W. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
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The Difference Between Control Group and Experimental Group
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What Is an Experiment? Definition and Design
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Home » experimental control important

What An Experimental Control Is And Why It’s So Important

Daniel Nelson

An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to “control.”

You may have heard of experimental control, but what is it? Why is an experimental control important? The function of an experimental control is to hold constant the variables that an experimenter isn’t interested in measuring.

This helps scientists ensure that there have been no deviations in the environment of the experiment that could end up influencing the outcome of the experiment, besides the variable they are investigating. Let’s take a closer look at what this means.

You may have ended up here to understand why a control is important in an experiment. A control is important for an experiment because it allows the experiment to minimize the changes in all other variables except the one being tested.

To start with, it is important to define some terminology.

Terminology Of A Scientific Experiment


Negative	The negative control variable is a variable or group where no response is expected
Positive	A positive control is a group or variable that receives a treatment with a known positive result
Randomization	A randomized controlled seeks to reduce bias when testing a new treatment
Blind experiments	In blind experiments, the variable or group does not know the full amount of information about the trial to not skew results
Double-blind experiments	A double-blind group is where all parties do not know which individual is receiving the experimental treatment

Randomization is important as it allows for more non-biased results in experiments. Random numbers generators are often used both in scientific studies as well as on 지노 사이트 to make outcomes fairer.

Scientists use the scientific method to ask questions and come to conclusions about the nature of the world. After making an observation about some sort of phenomena they would like to investigate, a scientist asks what the cause of that phenomena could be. The scientist creates a hypothesis, a proposed explanation that answers the question they asked. A hypothesis doesn’t need to be correct, it just has to be testable.

The hypothesis is a prediction about what will happen during the experiment, and if the hypothesis is correct then the results of the experiment should align with the scientist’s prediction. If the results of the experiment do not align with the hypothesis, then a good scientist will take this data into consideration and form a new hypothesis that can better explain the phenomenon in question.

Independent and Dependent Variables

In order to form an effective hypothesis and do meaningful research, the researcher must define the experiment’s independent and dependent variables . The independent variable is the variable which the experimenter either manipulates or controls in an experiment to test the effects of this manipulation on the dependent variable. A dependent variable is a variable being measured to see if the manipulation has any effect.

Photo: frolicsomepl via Pixabay, CC0

For instance, if a researcher wanted to see how temperature impacts the behavior of a certain gas, the temperature they adjust would be the independent variable and the behavior of the gas the dependent variable.

Control Groups and Experimental Groups

There will frequently be two groups under observation in an experiment, the experimental group, and the control group . The control group is used to establish a baseline that the behavior of the experimental group can be compared to. If two groups of people were receiving an experimental treatment for a medical condition, one would be given the actual treatment (the experimental group) and one would typically be given a placebo or sugar pill (the control group).

Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment. This is because there can always be outside factors that are influencing the behavior of the experimental group. The function of a control group is to act as a point of comparison, by attempting to ensure that the variable under examination (the impact of the medicine) is the thing responsible for creating the results of an experiment. The control group is holding other possible variables constant, such as the act of seeing a doctor and taking a pill, so only the medicine itself is being tested.

Why Are Experimental Controls So Important?

Experimental controls allow scientists to eliminate varying amounts of uncertainty in their experiments. Whenever a researcher does an experiment and wants to ensure that only the variable they are interested in changing is changing, they need to utilize experimental controls.

Experimental controls have been dubbed “controls” precisely because they allow researchers to control the variables they think might have an impact on the results of the study. If a researcher believes that some outside variables could influence the results of their research, they’ll use a control group to try and hold that thing constant and measure any possible influence it has on the results. It is important to note that there may be many different controls for an experiment, and the more complex a phenomenon under investigation is, the more controls it is likely to have.

Not only do controls establish a baseline that the results of an experiment can be compared to, they also allow researchers to correct for possible errors. If something goes wrong in the experiment, a scientist can check on the controls of the experiment to see if the error had to do with the controls. If so, they can correct this next time the experiment is done.

A Practical Example

Let’s take a look at a concrete example of experimental control. If an experimenter wanted to determine how different soil types impacted the germination period of seeds , they could set up four different pots. Each pot would be filled with a different soil type, planted with seeds, then watered and exposed to sunlight. Measurements would be taken regarding how long it took for the seeds to sprout in the different soil types.

Photo: Kaz via Pixabay, CC0

A control for this experiment might be to fill more pots with just the different types of soil and no seeds or to set aside some seeds in a pot with no soil. The goal is to try and determine that it isn’t something else other than the soil, like the nature of the seeds themselves, the amount of sun they were exposed to, or how much water they are given, that affected how quickly the seeds sprouted. The more variables a researcher controlled for, the surer they could be that it was the type of soil having an impact on the germination period.

Not All Experiments Are Controlled

“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” — Richard P. Feynman

While experimental controls are important , it is also important to remember that not all experiments are controlled. In the real world, there are going to be limitations on what variables a researcher can control for, and scientists often try to record as much data as they can during an experiment so they can compare factors and variables with one another to see if any variables they didn’t control for might have influenced the outcome. It’s still possible to draw useful data from experiments that don’t have controls, but it is much more difficult to draw meaningful conclusions based on uncontrolled data.

Though it is often impossible in the real world to control for every possible variable, experimental controls are an invaluable part of the scientific process and the more controls an experiment has the better off it is.

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Controlled Experiments: Methods, Examples & Limitations

What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the dependent variables.

Therefore, when the experiment is controlled, you can expect that the researcher will control all other variables except for the independent variables . This is done so that the other variables do not have an influence on the dependent variables.

In this article, we are going to consider controlled experiment, how important it is in a study, and how it can be designed. But before we dig deep, let us look at the definition of a controlled experiment.

What is a Controlled Experiment?

In a scientific experiment, a controlled experiment is a test that is directly altered by the researcher so that only one variable is studied at a time. The single variable being studied will then be the independent variable.

This independent variable is manipulated by the researcher so that its effect on the hypothesis or data being studied is known. While the researcher studies the single independent variable, the controlled variables are made constant to reduce or balance out their impact on the research.

To achieve a controlled experiment, the research population is mostly distributed into two groups. Then the treatment is administered to one of the two groups, while the other group gets the control conditions. This other group is referred to as the control group.

The control group gets the standard conditions and is placed in the standard environment and it also allows for comparison with the other group, which is referred to as the experimental group or the treatment group. Obtaining the difference between these two groups’ behavior is important because in any scientific experiment, being able to show the statistical significance of the results is the only criterion for the results to be accepted.

So to determine whether the experiment supports the hypothesis, or if the data is a result of chance, the researcher will check for the difference between the control group and experimental group. Then the results from the differences will be compared with the expected difference.

For example, a researcher may want to answer this question, do dogs also have a music taste? In case you’re wondering too, yes, there are existing studies by researchers on how dogs react to different music genres.

Back to the example, the researcher may develop a controlled experiment with high consideration on the variables that affect each dog. Some of these variables that may have effects on the dog are; the dog’s environment when listening to music, the temperature of the environment, the music volume, and human presence.

The independent variable to focus on in this research is the genre of the music. To determine if there is an effect on the dog while listening to different kinds of music, the dog’s environment must be controlled. A controlled experiment would limit interaction between the dog and other variables.

In this experiment, the researcher can also divide the dogs into two groups, one group will perform the music test while the other, the control group will be used as the baseline or standard behavior. The control group behavior can be observed along with the treatment group and the differences in the two group’s behavior can be analyzed.

What is an Experimental Control?

Experimental control is the technique used by the researcher in scientific research to minimize the effects of extraneous variables. Experimental control also strengthens the ability of the independent variable to change the dependent variable.

For example, the cause and effect possibilities will be examined in a well-designed and properly controlled experiment if the independent variable (Treatment Y) causes a behavioral change in the dependent variable (Subject X).

In another example, a researcher feeds 20 lab rats with an artificial sweetener and from the researcher’s observation, six of the rats died of dehydration. Now, the actual cause of death may be artificial sweeteners or an unrelated factor. Such as the water supplied to the rats being contaminated or the rats could not drink enough, or suffering a disease.

Read: Nominal, Ordinal, Interval & Ratio Variable + [Examples]

For a researcher, eliminating these potential causes one after the other will consume time, and be tedious. Hence, the researcher can make use of experimental control. This method will allow the researcher to divide the rats into two groups: one group will receive the artificial sweetener while the other one doesn’t. The two groups will be placed in similar conditions and observed in similar ways. The differences that now occur in morbidity between the two groups can be traced to the sweetener with certainty.

From the example above, the experimental control is administered as a form of a control group. The data from the control group is then said to be the standard against which every other experimental outcome is measured.

Purpose & Importance of Control in Experimentation

1. One significant purpose of experimental controls is that it allows researchers to eliminate various confounding variables or uncertainty in their research. A researcher will need to use an experimental control to ensure that only the variables that are intended to change, are changed in research.

2. Controlled experiments also allow researchers to control the specific variables they think might have an effect on the outcomes of the study. The researcher will use a control group if he/she believes some extra variables can form an effect on the results of the study. This is to ensure that the extra variable is held constant and possible influences are measured.

3. Controlled experiments establish a standard that the outcome of a study should be compared to, and allow researchers to correct for potential errors.

Read more: What are Cross-Sectional Studies: Examples, Definition, Types

Methods of Experimental Control

Here are some methods used to achieve control in experimental research

Use of Control Groups

Control groups are required for controlled experiments. Control groups will allow the researcher to run a test on fake treatment, and comparable treatment. It will also compare the result of the comparison with the researcher’s experimental treatment. The results will allow the researcher to understand if the treatment administered caused the outcome or if other factors such as time, or others are involved and whether they would have yielded the same effects.

For an example of a control group experiment, a researcher conducting an experiment on the effects of colors in advertising, asked all the participants to come individually to a lab. In this lab, environmental conditions are kept the same all through the research.

For the researcher to determine the effect of colors in advertising, each of the participants is placed in either of the two groups: the control group or the experimental group.

In the control group, the advertisement color is yellow to represent the clothing industry while blue is given as the advertisement color to the experimental group to represent the clothing industry also. The only difference in these two groups will be the color of the advertisement, other variables will be similar.

Use of Masking (blinding)

Masking occurs in an experiment when the researcher hides condition assignments from the participants. If it’s double-blind research, both the researcher and the participants will be in the dark. Masking or blinding is mostly used in clinical studies to test new treatments.

Masking as a control measure takes place because sometimes, researchers may unintentionally influence the participants to act in ways that support their hypotheses. In another scenario, the goal of the study might be revealed to the participants through the study environment and this may influence their responses.

Masking, however, blinds the participants from having a deeper knowledge of the research whether they’re in the control group or the experimental group. This helps to control and reduce biases from either the researcher or the participants that could influence the results of the study.

Use of Random Assignment

Random assignment or distribution is used to avoid systematic differences between participants in the experimental group and the control group. This helps to evenly distribute extraneous participant variables, thereby making the comparison between groups valid. Another usefulness of random assignment is that it shows the difference between true experiments from quasi-experiments.

Learn About: Double-Blind Studies in Research: Types, Pros & Cons

How to Design a Controlled Experiment

For a researcher to design a controlled experiment, the researcher will need:

A hypothesis that can be tested.
One or more independent variables can be changed or manipulated precisely.
One or more dependent variables can be accurately measured.

Then, when the researcher is designing the experiment, he or she must decide on:

How will the variables be manipulated?
How will control be set up in case of any potential confounding variables?
How large will the samples or participants included in the study be?
How will the participants be distributed into treatment levels?

How you design your experimental control is highly significant to your experiment’s external and internal validity.

Controlled Experiment Examples

1. A good example of a controlled group would be an experiment to test the effects of a drug. The sample population would be divided into two, the group receiving the drug would be the experimental group while the group receiving the placebo would be the control group (Note that all the variables such as age, and sex, will be the same).

The only significant difference between the two groups will be the taking of medication. You can determine if the drug is effective or not if the control group and experimental group show similar results.

2. Let’s take a look at this example too. If a researcher wants to determine the impact of different soil types on the germination period of seeds, the researcher can proceed to set up four different pots. Each of the pots would be filled with a different type of soil and then seeds can be planted on the soil. After which each soil pot will be watered and exposed to sunlight.

The researcher will start to measure how long it took for the seeds to sprout in each of the different soil types. Control measures for this experiment might be to place some seeds in a pot without filling the pot with soil. The reason behind this control measure is to determine that no other factor is responsible for germination except the soil.

Here, the researcher can also control the amount of sun the seeds are exposed to, or how much water they are given. The aim is to eliminate all other variables that can affect how quickly the seeds sprouted.

Experimental controls are important, but it is also important to note that not all experiments should be controlled and It is still possible to get useful data from experiments that are not controlled.

Explore: 21 Chrome Extensions for Academic Researchers in 2021

Problems with Controlled Experiments

It is true that the best way to test for cause and effect relationships is by conducting controlled experiments. However, controlled experiments also have some challenges. Some of which are:

Difficulties in controlling all the variables especially when the participants in your research are human participants. It can be impossible to hold all the extra variables constant because all individuals have different experiences that may influence their behaviors.
Controlled experiments are at risk of low external validity because there’s a limit to how the results from the research can be extrapolated to a very large population .
Your research may lack relatability to real world experience if they are too controlled and that will make it hard for you to apply your outcomes outside a controlled setting.

Control Group vs an Experimental Group

There is a thin line between the control group and the experimental group. That line is the treatment condition. As we have earlier established, the experimental group is the one that gets the treatment while the control group is the placebo group.

All controlled experiments require control groups because control groups will allow you to compare treatments, and to test if there is no treatment while you compare the result with your experimental treatment.

Therefore, both the experimental group and the control group are required to conduct a controlled experiment

FAQs about Controlled Experiments

Is the control condition the same as the control group?

The control group is different from the control condition. However, the control condition is administered to the control group.

What are positive and negative control in an experiment?

The negative control is the group where no change or response is expected while the positive control is the group that receives the treatment with a certainty of a positive result.

While the controlled experiment is beneficial to eliminate extraneous variables in research and focus on the independent variable only to cause an effect on the dependent variable.

Researchers should be careful so they don’t lose real-life relatability to too controlled experiments and also, not all experiments should be controlled.

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Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

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ScienceBuddies.org: Variables in Your Science Fair Project

About the Author

Benjamin Twist has worked as a writer, editor and consultant since 2007. He writes fiction and nonfiction for online and print publications, as well as offering one-on-one writing consultations and tutoring. Twist holds a Master of Arts in Bible exposition from Columbia International University.

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What is a Control in a Science Experiment?

C.D. Crowder
Categories : Great ideas for science fair projects
Tags : Homework help & study guides

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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Knowledge Base

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.

Examples of control variables
Research question	Control variables
Does soil quality affect plant growth?
Does caffeine improve memory recall?
Do people with a fear of spiders perceive spider images faster than other people?

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.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

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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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Grades 6-12
School Leaders

100 Last-Day-of-School Activities Your Students Will Love!

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50 Top 8th Grade Science Fair Projects and Classroom Activities

Find interesting ideas to engage all learners!

Collage of 8th grade science fair projects, including building a better lightbulb and guiding a plant through a light maze

Whether your students are preparing for the science fair or you’re looking for classroom ideas to grab their interest, we’ve got the answers! Find lots of 8th grade science fair projects across a spectrum of topics and difficulty levels (including plenty of easy science fair project ideas). Plus, check out fun classroom demos and hands-on experiments and activities your students will love.

To make it easier to find classroom activities or science fair projects for 8th graders, we’ve rated all the projects and activities by difficulty and the materials needed:

Difficulty:

Easy: Low or no-prep experiments you can do pretty much anytime
Medium: These take a little more setup or a longer time to complete
Advanced: Experiments like these take a fairly big commitment of time or effort
Basic: Simple items you probably already have around the house
Medium: Items that you might not already have but are easy to get your hands on
Advanced: These require specialized or more expensive supplies to complete

Biology and Life Science 8th Grade Science Fair Projects

Chemistry 8th grade science fair projects, physics and engineering 8th grade science fair projects, 8th grade science classroom demos, experiments, and hands-on activities.

Explore human behavior, plants and animals, the water cycle, and more with these 8th grade science fair project ideas.

Measure and compare lung capacity

Two eighth grade science students measuring the circumference of a blue balloon

Difficulty: Easy / Materials: Medium

This experiment combines math and biology to measure lung capacity using a balloon. There are a lot of interesting hypotheses students can form, document, and explore while taking these measurements.

Learn more: Measuring Lung Capacity at Blog She Wrote

Guide a growing plant through a maze

A plant in a cardboard box, growing in a twisted pattern through holes toward light at the top

Difficulty: Medium / Materials: Basic

Prove that plants really do seek out the light by setting up a simple or complex maze. This is a simple 8th grade science project with really cool results.

Learn more: Plant Light Maze at KiwiCo

Explore symbiosis with nitrogen-fixing bacteria

Frozen peas next to two plant containers labeled control and bacteria

Difficulty: Medium / Materials: Medium

Many plants depend on nitrogen for growth, but how important is it? This science project compares the growth of pea plants with and without nitrogen-fixing bacteria.

Learn more: Nitrogen and Plants at Education.com

Test water quality

Water quality testing kit with TDS meter

A water-testing kit opens up limitless options for 8th grade science fair projects. Test the water quality of local streams, swimming pools, or even the taps at home.

Learn more: Water Quality Experiment at The Homeschool Scientist

Cast animal tracks

Plaster cast of dog footprint next to autumn leaves, dated 7/25/15

Explore wildlife biology by becoming an expert tracker! Learn to identify tracks and take casts. Turn this into an experiment by trying different methods to take casts, or use it as a method of identifying wildlife in the woods.

Learn more: Casting Animal Tracks at Blog She Wrote

Determine a plant’s favorite music

Play different types of music for plants, then observe and document any changes in the growth and development of the plants as they’re exposed to different genres of music.

Conduct fingerprint analysis

Large fingerprint in black ink on white paper

Budding forensic scientists will love this idea. Learn to dust for prints and try a technique called “fuming” for trickier surfaces. See if you can compare prints and make accurate matches in the classroom. You can buy a fingerprinting kit just for kids or use supplies from around the house.

Learn more: Fingerprinting at Home Science Tools

Examine the connection between personality and memory

Notecard labeled Personality Type INFJ and worksheet labeled Memory Tests

Do introverts have better memories than extroverts? This science project aims to find out. Round up some willing volunteers and administer the Myers-Briggs personality test, then challenge your subjects with a memory test. The results may or may not surprise you!

Learn more: Memory and Personality at Education.com

Measure algae growth

Mason jars filled with water and algae, along with other chemicals

Fertilizer runoff has become a serious cause of water pollution. In this experiment, students will see its effects firsthand and brainstorm ways to keep it in check.

Learn more: Algae and Pollution Experiment at Layers of Learning

Water plants with different liquids

A series of plants in glass jars, labeled

In this easy science fair project, kids water plants with different liquids, like rainwater, tap water, salt water, and even soda. They might be surprised at the results!

Learn more: Effecting Plant Growth at Calm the Chaos Parenting

Beakers and test tubes, pouring and mixing … do it all with these fun chemistry science fair project ideas for 8th graders.

Perform a starch test with iodine

Test tube with yellow liquid labeled neg, test tube with black liquid labeled pos, and stoppered bottle filled with iodine

This simple chemistry experiment uses iodine to determine the starch content of food items. Use the process to perform a variety of 8th grade science experiments related to food.

Learn more: Starch Test at Biology Notes for IGCSE

Keep your hands warm

Man rubbing hands together with plastic bag of black liquid in between

If you live in a chilly part of the world, chances are you’ve seen chemical hand warmers for sale. In this 8th grade science fair project, use oxidation to make your own hand warmer, and find other creative ways to use this heating process.

Learn more: Homemade Hand Warmer at Steve Spangler Science

Compare electrolytes in sports drinks

Sports-loving kids will enjoy the chance to learn just how many valuable electrolytes their favorite sports drinks contain. Compare them with water or orange juice for a cool science fair project. You’ll need a few special supplies, like a multimeter and an ohm resistor , but they’re not too expensive and they’re easy to find.

Turn juice into spheres

You’ll need a few special supplies for this experiment , but the results are so cool. Turn spherification into a science fair project by experimenting with different beverages and liquids.

Block the sun’s UV rays

Use color-changing UV beads to test the protective power of medicine bottles, hats, clothing, and more. This is an easy 8th grade science fair project with nearly endless possibilities.

Grow a carbon sugar snake

Tin pan of sand with large carbon snake growing out of it

Remember those little black pellets that fire up into long snakes on the 4th of July? This is the same concept but much bigger! The simple chemical reaction of sugar and baking soda makes it happen. Turn this into an 8th grade science fair project by varying the formula to create even bigger results!

Learn more: Carbon Sugar Snake at KiwiCo

Create a rainbow of flames

You can change the color of fire by adding chemicals found at your local grocery store—what a sight! How can you use these flame colors to determine the chemical content of other materials? Sounds like a cool 8th grade science fair project!

Get your laundry really clean

Container of OxiClean with beakers of liquid on a counter

Find out if all those laundry detergent commercials are really telling the truth with this 8th grade science fair experiment. Test their cleaning power on a variety of stains and fabrics, and analyze your results.

Learn more: Science of Cleaning Products at Steve Spangler Science

Study the effects of acid rain

Difficulty: Easy / Materials: Basic

In this project, students use chalk as a stand-in for stone to learn how acid rain affects buildings, statues, and more. Turn this into a science fair project by exploring ways to mitigate the effects of the acidity.

Extract bismuth from Pepto Bismol

Black mortar filled with pink powder and a pestle

Difficulty: Advanced / Materials: Advanced

This is the kind of project that really makes you feel like a scientist. Grinding tablets with a mortar and pestle, filtering in beakers, heating over a Bunsen burner … this is what chemistry is all about!

Learn more: Extracting Bismuth at Popular Science

Optimize fermentation temperature

A Hot Yeast Experiment. Bottle of fizzing liquid with a partially inflated green balloon attached to the top.

Delve into the mystery of how temperature affects the fermentation process and determine the optimum temperature for yeast development. (Test your hypothesis by baking a loaf of bread!)

Learn more: Hot Yeast at Elemental Blogging

Brew up some root beer

Bowl filled with root beer and dry ice, spilling over with white vapors

Who says science can’t be delicious ? Tinker with the basic root beer recipe to make it sweeter, fizzier, or better in any way you like!

Learn more: Root Beer Science at Steve Spangler Science

For those who love to build and tinker, try a science fair project that experiments with various physics concepts like energy, electricity, motion, and more.

Build a better light bulb

Simple lightbulb built from a glass jar, battery, and wires

First, use the steps at the link to build a simple light bulb with a jar, some wire, and a 6-volt battery . Then, turn it into an 8th grade science fair project by tinkering with the various materials to make a light bulb that lasts longer, burns brighter, or is powered by an alternative source.

Learn more: Build a Light Bulb at 123 Homeschool 4 Me

Test the strength of interleaved paper

Paper seems smooth and slides apart easily, right? Not when you add friction into the mix! Mythbusters was amazed at how much strength it took to pull apart two interleaved phone books. Try this with smaller books for an 8th grade science fair project that people won’t believe!

Stand on a pile of paper cups

Student standing on top of a structure built from cardboard sheets and paper cups

Combine physics and engineering and challenge 8th grade science students to create a paper cup structure that can support their weight. This is a cool project for aspiring architects.

Learn more: Paper Cup Stack at Science Sparks

Cook up a tasty treat with solar energy

Students can design and build a solar oven, and then use it to cook food to compare the cooking time and temperature with a conventional oven. See if you can improve on the original design by changing up the materials or construction.

Investigate advanced properties of liquids

8th grade science project studying viscosity, surface tension, and temperature.

Do surface tension and viscosity decrease with increasing temperature? Find out in this 8th grade science fair project.

Learn more: Surface Tension and Viscosity at Education.com

Make a solar desalinator

Clean freshwater is a valuable commodity. Construct solar-powered desalination devices with readily available materials, and find the most effective desalination methods.

Engineer a roller coaster loop

Kids may have created marble roller coasters before, but have they ever built one with a loop-the-loop? They’ll have to experiment to find out which initial height gives a marble the speed it needs to complete the journey.

Capture a picture of lightning

Lightning pattern made on piece of acrylic with photocopier toner

Difficulty: Advanced / Materials: Medium

Lichtenberg figures capture the branching path of electricity as it travels through an object. You can make your own in a variety of ways, including burning it into wood or acrylic.

Learn more: Lichtenberg Figures at Science Notes

Crash cars for science

Collage of STEM car crash project images

This is a great class project for teachers, but it’s also excellent for an 8th grade science fair project. Build cars and crash-test them to learn the best methods of keeping passengers safe.

Learn more: STEM Car Crash Project at The Ardent Teacher

Discover the center of gravity

Wood craft stick balanced on end on a pencil, with orange pipe cleaner twisted around it

Once you find and maintain its center of gravity, almost any object will balance, even in surprising circumstances. Using this concept, what amazing objects can you balance and where?

Learn more: Center of Gravity at Rookie Parenting

Power up homemade batteries

Bottle of distilled white vinegar, paper towel, aluminum foil, duct tape, pennies, electrical wires, and voltmeter

Building batteries is a classic science experiment for any age. Make it into an 8th grade science fair project by trying different variables and exploring the amount of power you can produce.

Learn more: DIY Batteries at 123 Homeschool 4 Me

Assemble a spring balance scale

Apply Hooke’s law to find out if the stretching of a spring can be used to accurately measure the weight of objects. The materials are simple, but you’ll need patience and physics to calibrate a spring and use it to test weights.

Design a robotic hand

Model robotic hand made from paper, straws, and string

This is a project that can be tweaked by coming up with ways to improve upon the design. Can you build a hand that can pick up a ball? How about one that can pluck up a piece of string? So many possibilities!

Learn more: DIY Model Robot Hand STEM Activity at Mombrite

Build an infinity mirror

Experiment with optical illusions by creating a tunnel of lights that seems to stretch away into infinity. Eighth grade science students will learn about engineering and the physics of optics along the way.

Construct a Rube Goldberg machine

Create a machine to complete a simple task in the most complicated fashion! This is a neat 8th grade STEM fair project because it allows you to use a variety of physics concepts in a fun way.

Explore a wide variety of 8th grade science concepts with these fun and engaging activities.

Protect an egg in a crash

We love this spin on the classic egg-drop project. In this version, students build a structure to protect an egg during a collision with a wall, making the connection between crash tests and physics concepts.

Drop an egg to prove the first law of motion

Egg on top of a toilet paper tube, standing on a plate on a glass of water, with a man ready to hit the plate

This experiment looks like a magic trick, but it’s firmly grounded in Newton’s first law of motion. When you knock the pie tin out of the way, the egg falls straight into the glass thanks to inertia. (Worried about making a mess? Use plastic eggs instead.)

Learn more: Egg Drop Inertia Challenge at Steve Spangler Science

Break out the leaf blower to teach Bernoulli’s principle

Eighth grade science students have probably seen a Bernoulli demo or two, often with straws and Ping-Pong balls. So grab their attention by trying it with a leaf blower and a beach ball instead!

Assemble a Newton’s cradle

Newton's Cradle built of wood craft sticks, yarn, and marbles

Newton’s cradle is a fascinating way of demonstrating momentum and energy transfer. Follow the directions at the link to build one, or challenge 8th grade science students to experiment with their own construction methods.

Learn more: Newton’s Cradle at Babble Dabble Do

Extinguish a candle without blowing it out

Combine an acid/base experiment with some fire science in this really popular classroom science demo. It seems like magic, but it’s just science!

Relight a candle without touching it

Student's hand holding a lighter over a candle that has just been blown out

Tell students you’re going to relight a candle without touching the flame to the wick. The results will boggle their minds!

Learn more: Magic Traveling Flame at Steve Spangler Science

Demonstrate the “unpoppable” balloon

Your students won’t believe you when you say you can hold a balloon up to a flame without popping it. Use the conductivity of water to prove your point.

Extract your own DNA

Test tube with cloudy liquid and small white floating strand

DNA is the blueprint of life, and you’ll be surprised at how easy it is to extract your own with a few simple supplies. Preserve it in alcohol in the freezer when you’re done.

Learn more: How To Extract DNA at Home at Home Science Tools

Build a trash can air cannon

This is such a fun way to demonstrate an air vortex! It takes a little effort to build the air cannon, but you can use it year after year for amazing 8th grade science demos.

Separate water into hydrogen and oxygen

Eighth grade girl wearing goggles, looking at a container of water with test tubes and electric wires

Use electrolysis to prove that water really is made up of hydrogen and oxygen. It’s a simple concept but one that never fails to amaze.

Learn more: Separating Water at Navigating by Joy

Assemble a ring of Pringles

Everybody loves an edible STEM challenge! Here’s one that seems simple but takes some time to work out: Build a ring of Pringles chips without using any other materials.

Construct a cup holder

Student showing a foil platform balanced on drinking straws, holding two yellow plastic cups of water

Can your 8th grade science students build a device to stabilize and carry two cups of water, using only a few simple supplies? Oh, and can they manage it in just 5 minutes? This timed challenge pushes their creative engineering limits!

Learn more: Cup Holder STEM Challenge at Homeschool Creations

Navigate a light maze

Here’s the STEM challenge: Bounce a beam of light around a corner past an obstacle. Increase the difficulty by adding more obstacles and variables.

Engage your 8th grade science students further with these 24 Science Kits for Middle and High School That Make Hands-On Lessons Easy .

Plus, sign up for our newsletters to get all the latest teaching tips and ideas straight to your inbox..

Find engaging 8th grade science fair projects, including plenty of easy options, plus fun demos, experiments, and hands-on activities.

Collage of 7th grade science projects, including Oreo mitosis models and electroplating a coin

50 Sensational 7th Grade Science Fair Projects and Classroom Activities

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The Fight Over the Next Pandemic

The deadline for a new international pandemic plan was last week. so far, negotiations have failed..

This transcript was created using speech recognition software. While it has been reviewed by human transcribers, it may contain errors. Please review the episode audio before quoting from this transcript and email [email protected] with any questions.

From The New York Times, I’m Michael Barbaro. This is “The Daily.”

[MUSIC PLAYING]

Today, at the height of the COVID pandemic, nearly 200 countries started negotiating a plan to ensure they did better when the next pandemic inevitably arrives. Their deadline for that plan was last week.

My colleague Apoorva Mandavilli explains why so far, those negotiations have failed.

It’s Thursday, June 6.

So, Apoorva, something that was supposed to happen and happen right now that I think most of us didn’t even was ever in the works hasn’t happened. And that’s a global plan for the next pandemic. So tell us this entire story.

Think back to 2021, the very worst days of COVID when we had thousands of people dying in the US and in the rest of the world. There was just so much confusion about whether to wear masks or not, whether to close schools. And it was very difficult to think what any country should do.

And so in the middle of that chaos and confusion —

The Eagle has landed.

Carrying the hopes of a country, the first shipment of coronavirus vaccines reach Australian skies.

— we did get the vaccines.

You’re watching right now history being made, one of the first people in the entire country right here to get dose number two of the Pfizer vaccine.

Then all of a sudden, there was this hope. But the thing is that those vaccines were really mostly available in the richer countries.

Parts of Asia and Latin America have recorded a spike in COVID fatalities amid medical supply and vaccine shortages.

Few people in Africa have been vaccinated. Some countries don’t have any vaccines at all.

So we in the United States and a lot of countries in the European Union and some of the other high and middle income countries had the vaccines.

Rich countries have enough doses to vaccinate everyone nearly three times over, whilst poor countries don’t have enough to even reach health workers and people at risk.

But elsewhere in the world, there were no vaccines really. It became obvious to some low and middle income countries that they were not going to do very well in this pandemic. There were all these advanced purchase orders from the richer countries. And they were having some very tough negotiations with pharma companies that were charging them more than they were charging the rich countries.

And by the end of that horrible, horrible year, more than 90 percent of people in the richer countries had had two doses of vaccine. But 2 percent of people in low income countries had had any vaccines. So that really just striking inequity made people realize this was just a mess. We did not know how to deal with the pandemic.

The time to act is now.

So in December 2021, by the end of this year of inequity —

We must not allow the memories of this crisis to fade and go back to business as usual.

— the World Health Organization brought together all the countries —

The impacts on our societies, economies, and health, especially for the poor and the most vulnerable, are too significant.

— and launched this process to come up with a playbook to really think about how all the countries of the world need to prevent and respond to the next pandemic and do it in a way that would protect everybody, rich and poor, across the world. And the WHO decided that this discussion could not be just an informal conversation between health ministers, that this needed to be an international treaty, a legally binding treaty so that every country has to take this very seriously and everybody agrees on how to do this next time.

Hmm. So at the very height of COVID’s awfulness, these countries in the WHO are saying, we know you all are very, very busy fighting this pandemic. It is taking up all your time and energy. But we need you to now start to think about how badly this is going and not just fight the current pandemic but start planning on a better way to fight the next one. That’s kind of a big ask.

It is a big ask, but what is the alternative? That we come to the next pandemic and have a repeat of all of the chaos and confusion we saw during COVID? So I think it was an acknowledgment that we needed it. We needed to come up with a plan. And it became obvious that part of that plan needed to be a way to repair the mistrust that had formed between low income countries and high income countries and that without repairing that, we just did not really stand a good chance of fighting the next pandemic.

Right. And, of course, the thing about a global pandemic is that any weak link, any country that’s not doing its part or getting what it needs, becomes a problem for every other country. That’s the nature of a pandemic. We need — we talked about this with you, we talked about this with our colleagues throughout the pandemic — a system where there’s a strong program and plan in every country so that the virus can be stamped out.

Exactly. I mean, in the United States, more people died because of variants than they did because of the original virus. And a lot of those variants started in countries that did not have access to vaccines.

OK, so what do these talks start to actually look like? And just how many countries end up being involved in them?

So all of the countries that are member states of the World Health Organization were involved in this. 194 countries.

And they all sent delegates to meet to draft something and then to discuss every aspect of it and try to come to a consensus. And the goal was to get that to a point where all the countries were ready to sign off on it by May 2024. They had meetings over a period of two and a half years to talk through this. Some sections they all agreed on pretty easily. You can imagine the general goals like, yes, we should have a good plan to fight a pandemic. Or yes, we should have good research on vaccines and drugs, things like that, the general sort of philosophical goals everybody agrees on.

Right. Principles are always the easiest thing to negotiate.

The easiest thing to negotiate. But then you start getting into how this happens, right? And it’s actually kind of interesting. In the draft, if you look at the drafts, they have areas that are green, which means everybody sort of agreed, and yellow, which means they’re starting to come to an agreement, their sort of general consensus, and then white, which means it’s really no agreement. They’re just not even on the same page. And when you look at what’s green across all of these drafts, the philosophical goal is green from the start, no problem.

The yellow started to come slowly, these areas of consensus, things like, for example, safety measures in the labs that work with dangerous viruses. And that’s not just because one of the theories about COVID is that the virus leaked from a lab. We know from long before COVID that lab safety is very important for making sure that those dangerous viruses don’t get out into the world. There is also agreement around how countries should do surveillance to see what outbreaks might be emerging. And some of that stuff is tricky.

Why is it tricky? I mean, isn’t there a pretty standard playbook for trying to detect a virus and what to do once you detect it?

Sure. But there are some things that are big sticking points like money. Not all countries have the resources to do the kind of surveillance that they need to do. And so who funds that? And then some countries have vested interests, like Argentina wouldn’t want any rules that forbid export of certain kinds of meat products because that’s a big part of their economy.

There are countries where live animal markets are a thing, and not just in China, which we’re all familiar with, is another origin theory for COVID. Lots of other countries rely on these markets. And they don’t want to have very strict rules about which animals can be held together and how densely packed they can or can’t be. So when you start to get into the details there, it is actually difficult to reach consensus on some of these things.

But they have made a lot of progress. And they have come to yellow and green on some important things like that every country should have a health care workforce trained to respond to a pandemic, that they should make best efforts to have local production of things like vaccines and drugs, and that they should provide all of these resources to their own citizens. Things like that, those are all under agreement. They’re all green now.

So what exactly is holding these negotiations back? What ends up being the biggest remaining conflict?

It won’t surprise you to hear, Michael, that the biggest conflict is exactly what all of this began with, which is the lack of access that low income countries have to things like vaccines.

There have been interesting proposals in the drafts and one in particular that would solve at least some of this issue. But it’s been very difficult to convince rich countries, middle income countries, and low income countries that that proposal would be of great benefit to everybody involved.

We’ll be right back. So, Apoorva, tell us about this particular proposal that could do a lot of work to solve the inequities at the center of these negotiations and why that proposal has created so much conflict.

The heart of the section that has really created the most conflict is whether low income countries get access to vaccines in a timely manner and at a cost that is affordable to them. And all the low income countries recognize that they don’t have a lot of bargaining power. They were treated pretty poorly by pharma companies during this past pandemic. And so they’ve been thinking about setting things up so that that does not happen again, that the next time around, they are not left behind.

Right. But like you said, they don’t have a lot of power to bargain.

They don’t. But there have been times when poor countries have come up with a way to make everybody else realize that they’re essential to this whole process. So let me give you an example of this that really, I think, illustrates how much everybody else needs the low income nations during an outbreak.

So in 2006, Indonesia was battling a bird flu outbreak. And they had been very dutifully sending samples of the virus that they had in their country to the World Health Organization labs to analyze. And that information helps pharma companies develop things like vaccines.

Or tests, right.

Or tests. And in this particular case, the Indonesian Health Ministry approached the World Health Organization to say, look, we’ve given you these samples. We have people dying in our country. And we need access to vaccines and drugs. And the WHO told them, sorry, we don’t directly distribute any of that. You have to talk to the manufacturers.

And this is where that leverage becomes really important because Indonesia did not actually have leverage with these pharma companies. And so the vaccine manufacturer told them that they would sell them vaccines but at commercial prices that that country cannot afford. And then a drug manufacturer told them that they did not have enough drugs to give Indonesia because richer countries had placed enough purchase orders that there was a delay of two years. So [LAUGHS]: Indonesia was so angry about all of this that they declined to share any more samples with the WHO.

So Indonesia basically says, we will never again make the mistake of promptly sharing information about a potentially deadly pathogen because we learned that we get nothing in return.

Right. And understanding that realization also has driven a lot of the conversation in the drafting of this treaty where low income countries have essentially said, we recognize that you need us to share these samples. But we are not going to do that unless you can promise to us that we will get some access to vaccines and drugs that you make based on the samples we give you. So we want something in return for the information we provide to you.

What is the specific proposal that comes from this realization?

Yeah, this proposal has created a lot of controversy, so there are versions of it. But the most recent one says essentially that if the low income countries share their samples with the WHO that pharma companies have to give the WHO 10 percent of the vaccines they make as a donation and then 10 percent either at a non-profit cost or just a deeply discounted rate also to the WHO. And then the WHO would distribute that 20 percent of vaccines that they get from the pharma companies to the countries that are in most need.

Hmm. So this proposal, which feels very innovative, is the ultimate manifestation of poor countries’ power in this dynamic. If they don’t get vaccines, then the big countries will never get the information about a virus that’s necessary for there to have ever been a vaccine. It’s really interesting.

It is. And this is the biggest chip that low income countries have. So they are not willing to budge on this. But guess who doesn’t like this? Pharmaceutical companies and the countries that really support the interests of the pharmaceutical companies. And that includes the United States, Germany, Switzerland, some of the big players, places where these companies are a big presence and a very powerful lobby.

What specifically have these pharmaceutical companies and the countries like the US that have so many of them said about this proposal?

So the countries, they are willing to give in principle and say that the pharmaceutical companies will voluntarily give some of the vaccines to the WHO, but they don’t want it mandated. Whereas the low income countries, they want it to be really codified so that there is no loophole. And the conversations have gone round and round on that one word, “voluntary.”

Apoorva, is it safe to assume that a country like the US, which, of course, has a booming and very profitable pharmaceutical industry, won’t sign on to these proposals unless that word “voluntary” is in the deal, that they cannot abide by one where it’s mandatory that these big pharmaceutical companies have to give up so much of their vaccine to poorer countries?

They are not going to say that in so many words, but yes. And the United States actually has come up with some very nice plans to help some of these low income countries set up infrastructure and be prepared for pandemics. But I think crossing pharmaceutical companies is not a place they will go.

Hmm. So is that really the only big obstacle left in these negotiations? Or is there anything else?

Oh, there’s lots more.

There has been so much misinformation and disinformation around this whole issue just like there has been about every aspect of COVID. And a lot of it centers around the hesitation and the opposition that many populist leaders have expressed. In the US, for example, there are Republican senators and governors who have come out against the treaty. And they say that this is a power grab by the WHO, that it is going to allow the director general of the WHO to tell the US what to do, whether to have mask mandates, whether to have vaccine mandates, none of which is true, by the way.

And in a bid to counter some of that misinformation, there is actually an explicit line in the treaty saying that the treaty respects the sovereignty of all the individual nations. They’ve tried to address that head on. But it hasn’t really made all of that chatter go away.

Mm-hmm. How much does this practically matter, the fact that a handful or perhaps more than a handful of Republicans in the US are skeptical of this and think ideologically speaking that it oversteps the bounds of what a treaty should do? I mean, ultimately, do they have any power over whether the US signs this treaty?

They do because delegates can agree to this treaty at the WHO, but everybody has to bring it back to their home countries. And in the US, the treaty then has to be approved for ratification by the Senate. You have to have a two thirds majority in the Senate say, yes, we agree to this treaty. So if you have a number of Republican senators who are absolutely opposed to it, it may not pass.

Mm. So it very much feels like so many of the issues that made everyone think this treaty was necessary, inequities between rich countries and poor countries and misinformation and ideological skepticism of how to handle a pandemic to begin with that really defined COVID for us, that those forces are now making it very hard for this treaty to actually be reached. They never really went away.

They never went away. It felt like there was about five seconds when everyone was united in thinking that we needed something different, and there was a lot of goodwill. But a lot of that has evaporated. And we’re getting very quickly to a point where people have forgotten what COVID looked like and felt like and what the devastation was like and have gone back to old positions on we don’t want to share. We don’t want to give anything away. Everything for us first. All of the thinking that led to the problems during COVID.

So what realistically happens now? And do you based on your reporting think that this treaty has any real chance of being completed and passed by the 194 countries involved in it?

Well, the draft was supposed to be finalized at the meeting last week of the World Health Assembly. And that didn’t happen. But they did set a deadline to say that the negotiations will continue. And then they’ll hope to have something done by next year’s meeting.

But there’s just so much in flux right now. There are elections all over the world. Who knows what Donald Trump will do if he gets elected? We know that he withdrew from the WHO the last time around. And he has even said that he may shut down the pandemic preparedness office in the White House. So he’s not particularly invested in this whole topic, this whole issue.

And in the meantime, we already have so many threats that are really picking up. For global health experts and for reporters like myself who watch all this stuff, it’s a bit alarming that we now have bird flu right here in the United States. And the next pandemic, pretty much every expert I talk to agrees it’s not a question of if, but when. And if we had had this treaty ready, if we can ever have this treaty ready, we would be so much better prepared for something like that to happen. But it just doesn’t seem all that likely right now.

Well, Apoorva, thank you very much. We appreciate it.

We’ll be right back.

Here’s what else you need to know today. In a last minute about face, New York Governor Kathy Hochul said she would block a long awaited tolling plan known as congestion pricing that was set to begin at the end of the month. The program, the first of its kind in the US, would have charged as much as $15 for cars entering the busiest parts of Manhattan. The goal was to alleviate traffic, reduce pollution, and raise money for the city’s aging subway system. But Hochul argued that the tolls threatened the city’s fragile economic recovery after the pandemic.

And on Thursday, former romantic partners of Hunter Biden, the president’s son, testified in a Delaware courtroom about the depths of his drug addiction and the toll that it took on them. The testimony, including how much Hunter Biden spent on drugs and the type of drugs he used, was designed to establish that he was a chronic drug abuser who lied when he claimed to be sober on an application for a handgun in 2018.

Today’s episode was produced by Alex Stern, Carlos Prieto, and Stella Tan with help from Will Reid and Rikki Novetsky. It was edited by Lexie Diao and Devon Taylor, contains original music by Marion Lozano, Pat McCusker, and Diane Wong, and was engineered by Chris Wood. Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly.

That’s it for “The Daily.” I’m Michael Barbaro. See you tomorrow.

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At the height of the Covid pandemic, nearly 200 countries started negotiating a plan to ensure they would do better when the next pandemic inevitably arrived. Their deadline for that plan was last week.

Apoorva Mandavilli, a science and global health reporter for The Times, explains why, so far, the negotiations have failed.

On today’s episode

Apoorva Mandavilli , a science and global health reporter for The New York Times.

Tedros Adhanom Ghebreyesus seated in front of a World Health Organization flag.

Background reading

Countries failed to agree on a treaty to prepare the world for the next pandemic before a major international meeting.

There are a lot of ways to listen to The Daily. Here’s how.

We aim to make transcripts available the next workday after an episode’s publication. You can find them at the top of the page.

The Daily is made by Rachel Quester, Lynsea Garrison, Clare Toeniskoetter, Paige Cowett, Michael Simon Johnson, Brad Fisher, Chris Wood, Jessica Cheung, Stella Tan, Alexandra Leigh Young, Lisa Chow, Eric Krupke, Marc Georges, Luke Vander Ploeg, M.J. Davis Lin, Dan Powell, Sydney Harper, Mike Benoist, Liz O. Baylen, Asthaa Chaturvedi, Rachelle Bonja, Diana Nguyen, Marion Lozano, Corey Schreppel, Rob Szypko, Elisheba Ittoop, Mooj Zadie, Patricia Willens, Rowan Niemisto, Jody Becker, Rikki Novetsky, John Ketchum, Nina Feldman, Will Reid, Carlos Prieto, Ben Calhoun, Susan Lee, Lexie Diao, Mary Wilson, Alex Stern, Sophia Lanman, Shannon Lin, Diane Wong, Devon Taylor, Alyssa Moxley, Summer Thomad, Olivia Natt, Daniel Ramirez and Brendan Klinkenberg.

Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly. Special thanks to Sam Dolnick, Paula Szuchman, Lisa Tobin, Larissa Anderson, Julia Simon, Sofia Milan, Mahima Chablani, Elizabeth Davis-Moorer, Jeffrey Miranda, Maddy Masiello, Isabella Anderson, Nina Lassam and Nick Pitman.

Apoorva Mandavilli is a reporter focused on science and global health. She was a part of the team that won the 2021 Pulitzer Prize for Public Service for coverage of the pandemic. More about Apoorva Mandavilli

COMMENTS

Control Group Vs Experimental Group In Science
Not all experiments require a control group, but a true "controlled experiment" does require at least one control group. For example, experiments that use a within-subjects design do not have a control group. In within-subjects designs, all participants experience every condition and are tested before and after being exposed to treatment.
Controlled experiments (article)
There are two groups in the experiment, and they are identical except that one receives a treatment (water) while the other does not. The group that receives the treatment in an experiment (here, the watered pot) is called the experimental group, while the group that does not receive the treatment (here, the dry pot) is called the control group.The control group provides a baseline that lets ...
Do experiments always need a control group?
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of ...
Control Groups and Treatment Groups
A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment.. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of comparing outcomes between different groups).
The Difference Between Control and Experimental Group
In an experiment, data from an experimental group is compared with data from a control group. These two groups should be identical in every respect except one: the difference between a control group and an experimental group is that the independent variable is changed for the experimental group, but is held constant in the control group.
Why control an experiment?
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 ...
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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 ...
Control Group Definition and Examples
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Control in experiments is critical for internal validity, which allows you to establish a cause-and-effect relationship between variables. Example: Experiment. You're studying the effects of colours in advertising. You want to test whether using green for advertising fast food chains increases the value of their products.
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An experiment without the proper controls is meaningless. Controls allow the experimenter to minimize the effects of factors other than the one being tested. It's how we know an experiment is testing the thing it claims to be testing. This goes beyond science — controls are necessary for any sort of experimental testing, no matter the ...
What Is a Control Group? Definition and Explanation
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.
What Is a Control Variable? Definition and Examples
Control Variable Examples. Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include: Duration of the experiment. Size and composition of containers. Temperature.
What Is a Controlled Experiment?
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12.7: Controls in Experiments. 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.
What Is a Controlled Experiment?
Controlled Experiment. A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable. A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
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Controlled experiments

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