experiment control vs constant

• Organizations
• Planning & Activities
• Product & Services
• Structure & Systems
• Career & Education
• Entertainment
• Fashion & Beauty
• Political Institutions
• SmartPhones
• Protocols & Formats
• Communication
• Web Applications
• Household Equipments
• Career and Certifications
• Diet & Fitness
• Mathematics & Statistics
• Processed Foods
• Vegetables & Fruits

Difference Between Constant and Control

• Categorized under Physics , Science | Difference Between Constant and Control

As scientists continue to figure out how nature works, they do so by use of experiments, with an aim of searching for cause and effect relationships. These relationships are used to explain why things happen and allow one to predict what will happen if a certain event occurs. The role of these experiments hence is to observe and measure how changes occur in relation to other things. In an experiment, the things that change are referred to as variables.  

What is a Constant?

These are values that do not change during experiments.  

For example, in an experiment where one wants to test how the growth of plants is affected by the amount of water, factors like type of soil, temperature, type of plant and sunlight all stay the same in the course of the experiment. These are hence referred to as the constants in this experiment, while the amount of water is the control.    

Other examples include freezing and boiling points of water, the speed of light,  

What is Controls?

A controlled variable is a variable that could change but is intentionally kept constant in order to clearly show the relationship between dependent and independent variables. It is also a variable that is not of primary interest and hence constitutes a third factor whose influence is to be controlled or eliminated. These variables either need to be kept constant during the experiment or be monitored and recorded. This ensures that their influence can be assessed. Most experiments have more than one control.  

An example used to explain control variable is the effect of fertilizers on a plants growth, whereby the growth of plants differs based on the amount of fertilizer used. The fertilizer, in this case, is the control variable.  

Other examples include time, pressure and temperature.

Similarities between Constant Vs. Control

• Both are important in experiments as they determine the outcome

Differences between Constant and Control

A constant variable does not change. A control variable on the other hand changes, but is intentionally kept constant throughout the experiment so as to show the relationship between dependent and independent variables.  

Primary interest

While the constant is the variable of primary interest, the control is not; hence its influence can be controlled or eliminated.  

Constant vs. Control: Comparison Table

Summary of Constant vs. Control

In an experiment, both constant and control variables are important as they influence the outcomes of the experiments.  

• Recent Posts
• Difference Between Profit Center and Investment Center - July 2, 2022
• Difference Between Anti-Trust and Anti-Competition - June 6, 2022
• Difference Between Stocktaking and Stock Control - June 6, 2022

Sharing is caring!

• Pinterest 4

Search DifferenceBetween.net :

• Difference Between Dependent Variables and Independent Variables
• Difference Between Study and Experiment
• Difference Between Experiment and Survey
• Difference Between Observational Study and Experiments
• Difference Between Science and Social Science

Cite APA 7 Njogu, T. (2019, June 24). Difference Between Constant and Control. Difference Between Similar Terms and Objects. http://www.differencebetween.net/science/difference-between-constant-and-control/. MLA 8 Njogu, Tabitha. "Difference Between Constant and Control." Difference Between Similar Terms and Objects, 24 June, 2019, http://www.differencebetween.net/science/difference-between-constant-and-control/.

Leave a Response

Name ( required )

Email ( required )

Please note: comment moderation is enabled and may delay your comment. There is no need to resubmit your comment.

Notify me of followup comments via e-mail

References :

Advertisments, more in 'physics'.

• Difference Between Velocity and Speed
• Difference Between Humidity and Temperature
• Difference Between Quantum Mechanics and General Relativity
• Difference Between Torque and Force
• Difference Between Leading and Lagging Power Factor

More in 'Science'

• Difference Between Rumination and Regurgitation
• Difference Between Pyelectasis and Hydronephrosis 
• Difference Between Cellulitis and Erysipelas
• Difference Between Suicide and Euthanasia
• Difference Between Vitamin D and Vitamin D3

Top Difference Betweens

Get new comparisons in your inbox:, most emailed comparisons, editor's picks.

• Difference Between MAC and IP Address
• Difference Between Platinum and White Gold
• Difference Between Civil and Criminal Law
• Difference Between GRE and GMAT
• Difference Between Immigrants and Refugees
• Difference Between DNS and DHCP
• Difference Between Computer Engineering and Computer Science
• Difference Between Men and Women
• Difference Between Book value and Market value
• Difference Between Red and White wine
• Difference Between Depreciation and Amortization
• Difference Between Bank and Credit Union
• Difference Between White Eggs and Brown Eggs

Sciencing_Icons_Science SCIENCE

Sciencing_icons_biology biology, sciencing_icons_cells cells, sciencing_icons_molecular molecular, sciencing_icons_microorganisms microorganisms, sciencing_icons_genetics genetics, sciencing_icons_human body human body, sciencing_icons_ecology ecology, sciencing_icons_chemistry chemistry, sciencing_icons_atomic & molecular structure atomic & molecular structure, sciencing_icons_bonds bonds, sciencing_icons_reactions reactions, sciencing_icons_stoichiometry stoichiometry, sciencing_icons_solutions solutions, sciencing_icons_acids & bases acids & bases, sciencing_icons_thermodynamics thermodynamics, sciencing_icons_organic chemistry organic chemistry, sciencing_icons_physics physics, sciencing_icons_fundamentals-physics fundamentals, sciencing_icons_electronics electronics, sciencing_icons_waves waves, sciencing_icons_energy energy, sciencing_icons_fluid fluid, sciencing_icons_astronomy astronomy, sciencing_icons_geology geology, sciencing_icons_fundamentals-geology fundamentals, sciencing_icons_minerals & rocks minerals & rocks, sciencing_icons_earth scructure earth structure, sciencing_icons_fossils fossils, sciencing_icons_natural disasters natural disasters, sciencing_icons_nature nature, sciencing_icons_ecosystems ecosystems, sciencing_icons_environment environment, sciencing_icons_insects insects, sciencing_icons_plants & mushrooms plants & mushrooms, sciencing_icons_animals animals, sciencing_icons_math math, sciencing_icons_arithmetic arithmetic, sciencing_icons_addition & subtraction addition & subtraction, sciencing_icons_multiplication & division multiplication & division, sciencing_icons_decimals decimals, sciencing_icons_fractions fractions, sciencing_icons_conversions conversions, sciencing_icons_algebra algebra, sciencing_icons_working with units working with units, sciencing_icons_equations & expressions equations & expressions, sciencing_icons_ratios & proportions ratios & proportions, sciencing_icons_inequalities inequalities, sciencing_icons_exponents & logarithms exponents & logarithms, sciencing_icons_factorization factorization, sciencing_icons_functions functions, sciencing_icons_linear equations linear equations, sciencing_icons_graphs graphs, sciencing_icons_quadratics quadratics, sciencing_icons_polynomials polynomials, sciencing_icons_geometry geometry, sciencing_icons_fundamentals-geometry fundamentals, sciencing_icons_cartesian cartesian, sciencing_icons_circles circles, sciencing_icons_solids solids, sciencing_icons_trigonometry trigonometry, sciencing_icons_probability-statistics probability & statistics, sciencing_icons_mean-median-mode mean/median/mode, sciencing_icons_independent-dependent variables independent/dependent variables, sciencing_icons_deviation deviation, sciencing_icons_correlation correlation, sciencing_icons_sampling sampling, sciencing_icons_distributions distributions, sciencing_icons_probability probability, sciencing_icons_calculus calculus, sciencing_icons_differentiation-integration differentiation/integration, sciencing_icons_application application, sciencing_icons_projects projects, sciencing_icons_news news.

• Share Tweet Email Print
• Home ⋅
• Science Fair Project Ideas for Kids, Middle & High School Students ⋅
• Probability & Statistics

What Are Constants & Controls of a Science Project Experiment?

What Is a Standardized Variable in Biology?

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

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

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

Example of an Independent Variable

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

Example of a Dependent Variable

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

Example of a Controlled Variable

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

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

Related Articles

Difference between manipulative & responding variable, what is a responding variable in science projects, definitions of control, constant, independent and dependent..., what is a constant in a science fair project, what are dependent, independent & controlled variables, what type of plants are best for science projects, how to collect data from a science project, what are independent & dependent variables in science..., can a science experiment have two manipulated variables, what is a constant in the scientific method, what is an independent variable in quantitative research, what is the difference between a control & a controlled..., what is the meaning of variables in research, what is a scientist who studies plants called, science fair project for testing different soils with..., why should you only test for one variable at a time..., what are the independent variables for a moldy bread..., what is the role of carotenoids in photosynthesis.

• ScienceBuddies: Variables in Your Science Fair Project
• ThoughtCo: What Is An Experiment?

About the Author

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

Find Your Next Great Science Fair Project! GO

Have a language expert improve your writing

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

• 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.

Table of contents

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

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

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

Control variables in experiments

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

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

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

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

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

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

• Marital status

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

Prevent plagiarism. Run a free check.

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.

Cite this Scribbr article

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

Bhandari, P. (2023, June 22). Control Variables | What Are They & Why Do They Matter?. Scribbr. Retrieved June 18, 2024, from https://www.scribbr.com/methodology/control-variable/

Is this article helpful?

Pritha Bhandari

Other students also liked, what is a controlled experiment | definitions & examples, independent vs. dependent variables | definition & examples, extraneous variables | examples, types & controls, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Have a language expert improve your writing

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

• Knowledge Base
• Methodology
• What Are Control Variables | Definition & Examples

What Are Control Variables? | Definition & Examples

Published on 4 May 2022 by Pritha Bhandari . Revised on 16 June 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 aims 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 randomisation or statistical control (e.g., to account for participant characteristics like age in statistical tests).

Table of contents

Why do control variables matter, how do you control a variable, control variable vs control group, 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.

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.

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

Prevent plagiarism, run a free check.

There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational 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 have more screen time during the day, and this might influence how alert they are in your study.

Standardised 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 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.

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 .

‘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.

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.

Cite this Scribbr article

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

Bhandari, P. (2023, June 16). What Are Control Variables? | Definition & Examples. Scribbr. Retrieved 18 June 2024, from https://www.scribbr.co.uk/research-methods/control-variables/

Is this article helpful?

Pritha Bhandari

Other students also liked, types of variables in research | definitions & examples, controlled experiments | methods & examples of control, a quick guide to experimental design | 5 steps & examples.

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.

The JoVE video player is compatible with HTML5 and Adobe Flash. Older browsers that do not support HTML5 and the H.264 video codec will still use a Flash-based video player. We recommend downloading the newest version of Flash here, but we support all versions 10 and above.

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

Get cutting-edge science videos from J o VE sent straight to your inbox every month.

mktb-description

We use cookies to enhance your experience on our website.

By continuing to use our website or clicking “Continue”, you are agreeing to accept our cookies.

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

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

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

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:

Want to join the conversation.

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

Constant vs Control: Difference and Comparison

Key Takeaways A constant is a factor or variable in an experiment or study that remains unchanged throughout the process, ensuring that changes observed in the outcome can be attributed to the manipulated variable. A control is a group or condition in an experiment or study that serves as a baseline or reference point, allowing researchers to compare the effects of the manipulated variable on the experimental group. Both constants and controls are essential components of a well-designed scientific experiment, ensuring accurate and reliable results by minimizing confounding factors and allowing for clear comparisons.

Constant vs Control

Similar reads, comparison table.

What is Constant?

What is control, main differences between constant and control.

Last Updated : 11 June, 2023

Share this post!

25 thoughts on “constant vs control: difference and comparison”.

This article was really informative and it provided a clear and detailed explanation of constants and controls in scientific experiments. I loved the comparison table!

I found the examples provided to be very helpful in understanding the concept of constant and control variables. Great article!

I agree, it was very informative. The comparison table was a great way to understand the differences between constant and control variables.

The explanation of constant and control variables was meticulous and well thought out, and I appreciated the attention to detail.

I agree, the article was very detailed and provided a holistic understanding of constant and control variables in scientific experiments.

The comparison table was a fantastic addition to the article, and I feel like I have a much better understanding of constants and controls now. Thank you!

I completely agree. The comparisons made the information easy to grasp and remember.

The article was spot-on in explaining the differences between constants and controls, and I found it to be very educational.

I think the article was astutely written and provided a deep dive into the concepts of constants and controls in scientific experiments.

I completely agree. The article delved into the intricacies of constants and controls and provided a comprehensive perspective.

The article was well-researched and presented, and I feel like it enhanced my understanding of constants and controls.

The article provided an insightful comparison between constant and control variables, and I appreciated the real-life examples given to explain the concepts.

I was thoroughly impressed by the detailed explanation of constants and controls. It was very well-presented.

I found the information about constants and controls to be very enlightening and applicable to various experiments. Great read!

The examples provided were really helpful and I think this article will be beneficial to anyone looking to understand constant and control variables in experiments.

The article was excellent, and the examples provided a practical understanding of constant and control variables.

I couldn’t agree more. The real-life examples made it easier to comprehend the concepts.

The article was comprehensive and informative, and I think it did a great job of elucidating the concepts of constant and control variables.

I found the article to be enlightening and well-researched. It provided a thorough understanding of constants and controls.

I think the article was clear and concise, and did a great job of explaining the significance of constants and controls in scientific experiments. Well done!

I couldn’t agree more. The article was well-written and provided a thorough understanding of constant and control variables.

The article was very well-written and provided a systematic breakdown of constant and control variables, which was incredibly beneficial.

The systematic explanation of constant and control variables was impressive and easy to comprehend. The article was a worthwhile read.

The comparisons and real-life examples really made the concepts of constant and control variables crystal clear. I thoroughly enjoyed reading this article.

I agree, the real-life examples were very insightful and contributed to a better understanding of the topic.

Leave a Comment Cancel reply

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Independent and Dependent Variables Examples

The independent and dependent variables are key to any scientific experiment, but how do you tell them apart? Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them.

Independent Variable

The independent variable is the factor the researcher changes or controls in an experiment. It is called independent because it does not depend on any other variable. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “ control variable ,” which is variable that is held constant so it won’t influence the outcome of the experiment.

Dependent Variable

The dependent variable is the factor that changes in response to the independent variable. It is the variable that you measure in an experiment. The dependent variable may be called the “responding variable.”

Examples of Independent and Dependent Variables

Here are several examples of independent and dependent variables in experiments:

• In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score.
• You want to know which brand of fertilizer is best for your plants. The brand of fertilizer is the independent variable. The health of the plants (height, amount and size of flowers and fruit, color) is the dependent variable.
• You want to compare brands of paper towels, to see which holds the most liquid. The independent variable is the brand of paper towel. The dependent variable is the volume of liquid absorbed by the paper towel.
• You suspect the amount of television a person watches is related to their age. Age is the independent variable. How many minutes or hours of television a person watches is the dependent variable.
• You think rising sea temperatures might affect the amount of algae in the water. The water temperature is the independent variable. The mass of algae is the dependent variable.
• In an experiment to determine how far people can see into the infrared part of the spectrum, the wavelength of light is the independent variable and whether the light is observed is the dependent variable.
• If you want to know whether caffeine affects your appetite, the presence/absence or amount of caffeine is the independent variable. Appetite is the dependent variable.
• You want to know which brand of microwave popcorn pops the best. The brand of popcorn is the independent variable. The number of popped kernels is the dependent variable. Of course, you could also measure the number of unpopped kernels instead.
• You want to determine whether a chemical is essential for rat nutrition, so you design an experiment. The presence/absence of the chemical is the independent variable. The health of the rat (whether it lives and reproduces) is the dependent variable. A follow-up experiment might determine how much of the chemical is needed. Here, the amount of chemical is the independent variable and the rat health is the dependent variable.

How to Tell the Independent and Dependent Variable Apart

If you’re having trouble identifying the independent and dependent variable, here are a few ways to tell them apart. First, remember the dependent variable depends on the independent variable. It helps to write out the variables as an if-then or cause-and-effect sentence that shows the independent variable causes an effect on the dependent variable. If you mix up the variables, the sentence won’t make sense. Example : The amount of eat (independent variable) affects how much you weigh (dependent variable).

This makes sense, but if you write the sentence the other way, you can tell it’s incorrect: Example : How much you weigh affects how much you eat. (Well, it could make sense, but you can see it’s an entirely different experiment.) If-then statements also work: Example : If you change the color of light (independent variable), then it affects plant growth (dependent variable). Switching the variables makes no sense: Example : If plant growth rate changes, then it affects the color of light. Sometimes you don’t control either variable, like when you gather data to see if there is a relationship between two factors. This can make identifying the variables a bit trickier, but establishing a logical cause and effect relationship helps: Example : If you increase age (independent variable), then average salary increases (dependent variable). If you switch them, the statement doesn’t make sense: Example : If you increase salary, then age increases.

How to Graph Independent and Dependent Variables

Plot or graph independent and dependent variables using the standard method. The independent variable is the x-axis, while the dependent variable is the y-axis. Remember the acronym DRY MIX to keep the variables straight: D = Dependent variable R = Responding variable/ Y = Graph on the y-axis or vertical axis M = Manipulated variable I = Independent variable X = Graph on the x-axis or horizontal axis

• Babbie, Earl R. (2009). The Practice of Social Research (12th ed.) Wadsworth Publishing. ISBN 0-495-59841-0.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 978-0-521-29925-1.
• Gauch, Hugh G. Jr. (2003). Scientific Method in Practice . Cambridge University Press. ISBN 978-0-521-01708-4.
• Popper, Karl R. (2003). Conjectures and Refutations: The Growth of Scientific Knowledge . Routledge. ISBN 0-415-28594-1.

Related Posts

What Is the Difference Between a Control Variable and Control Group?

Hiya Images/Corbis/VCG/Getty Images

• Scientific Method
• Chemical Laws
• Periodic Table
• Projects & Experiments
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

In experiments, controls are factors that you hold constant or don't expose to the condition you are testing. By creating a control, you make it possible to determine whether the variables alone are responsible for an outcome. Although control variables and the control group serve the same purpose, the terms refer to two different types of controls which are used for different kinds of experiments.

Why Experimental Controls Are Necessary

A student places a seedling in a dark closet, and the seedling dies. The student now knows what happened to the seedling, but he doesn't know why. Perhaps the seedling died from lack of light, but it might also have died because it was already sickly, or because of a chemical kept in the closet, or for any number of other reasons. 

In order to determine why the seedling died, it is necessary to compare that seedling's outcomes to another identical seedling outside the closet. If the closeted seedling died while the seedling kept in sunshine stayed alive, it's reasonable to hypothesize that darkness killed the closeted seedling. 

Even if the closeted seedling died while the seedling placed in sunshine lived, the student would still have unresolved questions about her experiment. Might there be something about the particular seedlings that caused the results she saw? For example, might one seedling have been healthier than the other to start with?

To answer all of her questions, the student might choose to put several identical seedlings in a closet and several in the sunshine. If at the end of a week, all of the closeted seedlings are dead while all of the seedlings kept in​ the sunshine are alive, it is reasonable to conclude that the darkness killed the seedlings.

Definition of a Control Variable

A control variable is any factor you control or hold constant during an experiment. A control variable is also called a controlled variable or constant variable. 

If you are studying the effect of the amount of water on seed germination, control variables might include temperature, light, and type of seed. In contrast, there may be variables you can't easily control, such as humidity, noise, vibration, and magnetic fields.

Ideally, a researcher wants to control every variable, but this isn't always possible. It's a good idea to note all recognizable variables in a lab notebook for reference.

Definition of a Control Group

A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable .

In an experiment to determine whether zinc helps people recover faster from a cold, the experimental group would be people taking zinc, while the control group would be people taking a placebo (not exposed to extra zinc, the independent variable).

A controlled experiment is one in which every parameter is held constant except for the experimental (independent) variable. Usually, controlled experiments have control groups. Sometimes a controlled experiment compares a variable against a standard.

• Difference Between Independent and Dependent Variables
• Examples of Independent and Dependent Variables
• Null Hypothesis Examples
• The Difference Between Control Group and Experimental Group
• Random Error vs. Systematic Error
• What Are These Tiny Black Bugs in My House?
• What Is a Controlled Experiment?
• The Role of a Controlled Variable in an Experiment
• What Is an Experiment? Definition and Design
• What are Controlled Experiments?
• What Is a Control Group?
• Understanding Experimental Groups
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• Scientific Variable
• What Is a Double Blind Experiment?

Control Group vs Experimental Group

Julia Simkus

Editor at Simply Psychology

BA (Hons) Psychology, Princeton University

Julia Simkus is a graduate of Princeton University with a Bachelor of Arts in Psychology. She is currently studying for a Master's Degree in Counseling for Mental Health and Wellness in September 2023. Julia's research has been published in peer reviewed journals.

Learn about our Editorial Process

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.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

In a controlled experiment , scientists compare a control group, and an experimental group is identical in all respects except for one difference – experimental manipulation.

Differences

Unlike the experimental group, the control group is not exposed to the independent variable under investigation. So, it 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.

Almost all experimental studies are designed to include a control group and one or more experimental groups. In most cases, participants are randomly assigned to either a control or experimental group.

Because participants are randomly assigned to either group, we can assume that the groups are identical except for manipulating the independent variable in the experimental group.

It is important that every aspect of the experimental environment is the same and that the experimenters carry out the exact same procedures with both groups so researchers can confidently conclude that any differences between groups are actually due to the difference in treatments.

Control Group

A control group consists of participants who do not receive any experimental treatment. The control participants serve as a comparison group.

The control group is matched as closely as possible to the experimental group, including age, gender, social class, ethnicity, etc.

The difference between the control and experimental groups is that the control group is not exposed to the independent variable , which is thought to be the cause of the behavior being investigated.

Researchers will compare the individuals in the control group to those in the experimental group to isolate the independent variable and examine its impact.

The control group is important because it serves as a baseline, enabling researchers to see what impact changes to the independent variable produce and strengthening researchers’ ability to draw conclusions from a study.

Without the presence of a control group, a researcher cannot determine whether a particular treatment truly has an effect on an experimental group.

Control groups are critical to the scientific method as they help ensure the internal validity of a study.

Assume you want to test a new medication for ADHD . One group would receive the new medication, and the other group would receive a pill that looked exactly the same as the one that the others received, but it would be a placebo. The group that takes the placebo would be the control group.

Types of Control Groups

Positive control group.

• A positive control group is an experimental control that will produce a known response or the desired effect.
• A positive control is used to ensure a test’s success and confirm an experiment’s validity.
• For example, when testing for a new medication, an already commercially available medication could serve as the positive control.

Negative Control Group

• A negative control group is an experimental control that does not result in the desired outcome of the experiment.
• A negative control is used to ensure that there is no response to the treatment and help identify the influence of external factors on the test.
• An example of a negative control would be using a placebo when testing for a new medication.

Experimental Group

An experimental group consists of participants exposed to a particular manipulation of the independent variable. These are the participants who receive the treatment of interest.

Researchers will compare the responses of the experimental group to those of a control group to see if the independent variable impacted the participants.

An experiment must have at least one control group and one experimental group; however, a single experiment can include multiple experimental groups, which are all compared against the control group.

Having multiple experimental groups enables researchers to vary different levels of an experimental variable and compare the effects of these changes to the control group and among each other.

Assume you want to study to determine if listening to different types of music can help with focus while studying.

You randomly assign participants to one of three groups: one group that listens to music with lyrics, one group that listens to music without lyrics, and another group that listens to no music.

The group of participants listening to no music while studying is the control group, and the groups listening to music, whether with or without lyrics, are the two experimental groups.

Frequently Asked Questions

1. what is the difference between the control group and the experimental group in an experimental study.

Put simply; an experimental group is a group that receives the variable, or treatment, that the researchers are testing, whereas the control group does not. These two groups should be identical in all other aspects.

2. What is the purpose of a control group in an experiment

A control group is essential in experimental research because it:

Provides a baseline against which the effects of the manipulated variable (the independent variable) can be measured.

Helps to ensure that any changes observed in the experimental group are indeed due to the manipulation of the independent variable and not due to other extraneous or confounding factors.

Helps to account for the placebo effect, where participants’ beliefs about the treatment can influence their behavior or responses.

In essence, it increases the internal validity of the results and the confidence we can have in the conclusions.

3. Do experimental studies always need a control group?

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.

These experimental designs tend to have weaker internal validity as it is more difficult for a researcher to be confident that the outcome was caused by the experimental treatment and not by a confounding variable.

4. Can a study include more than one control group?

Yes, studies can include multiple control groups. For example, if several distinct groups of subjects do not receive the treatment, these would be the control groups.

5. How is the control group treated differently from the experimental groups?

The control group and the experimental group(s) are treated identically except for one key difference: exposure to the independent variable, which is the factor being tested. The experimental group is subjected to the independent variable, whereas the control group is not.

This distinction allows researchers to measure the effect of the independent variable on the experimental group by comparing it to the control group, which serves as a baseline or standard.

Bailey, R. A. (2008). Design of Comparative Experiments. Cambridge University Press. ISBN 978-0-521-68357-9.

Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.

Related Articles

Research Methodology

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Thematic Analysis: A Step by Step Guide

Metasynthesis Of Qualitative Research

Grounded Theory In Qualitative Research: A Practical Guide

• Sourcebook Home

Science Teaching Series

• The Sourcebook for Teaching Science
• Hands-On Physics Activities
• Hands-On Chemistry Activities

Internet Resources

I. developing scientific literacy.

• 1 - Building a Scientific Vocabulary
• 2 - Developing Science Reading Skills
• 3 - Developing Science Writing Skills
• 4 - Science, Technology & Society

II. Developing Scientific Reasoning

• 5 - Employing Scientific Methods
• 6 - Developing Scientific Reasoning
• 7 - Thinking Critically & Misconceptions

III. Developing Scientific Understanding

• 8 - Organizing Science Information
• 9 - Graphic Oganizers for Science
• 10 - Learning Science with Analogies
• 11 - Improving Memory in Science
• 12 - Structure and Function in Science
• 13 - Games for Learning Science

IV. Developing Scientific Problem Solving

• 14 - Science Word Problems
• 15 - Geometric Principles in Science
• 16 - Visualizing Problems in Science
• 17 - Dimensional Analysis
• 18 - Stoichiometry

V. Developing Scientific Research Skills

• 19 - Scientific Databases
• 20 - Graphing & Data Analysis
• 21 - Mapping & Visualizing Data
• 22 - Science Inquiry & Research
• 23 - Science Projects & Fairs

VI. Resources for Teaching Science

• 24 - Science Curriculum & Instruction
• 25 - Planning Science Instruction
• 26 - The Science Laboratory
• 27 - Science Reference Information

Experimental Design: Glossary

Constant - The factors that do not change during the experiment. Control - The control is the group that serves as a standard of comparison. It is exposed to the same conditions as the experimental group, except for the variable being tested. Dependent Variable - A dependent variable is a factor that may change as a result of changes in the independent variable. Hypothesis - A hypothesis is a tentative explanation for a phenomenon, and is used as a basis for further investigation. It is a specific statement of prediction and describes what you expect to happen in a study. Independent Variable - An independent variable is a factor that is intentionally varied by the experimenter in order to see if it affects the dependent variable. Population - The group to which the results of an experiment can be generalized. Reliability : Reliability refers to the accuracy and consistency of a measurement or test. Randomization - Procedure to ensure that every member of a target population has an equal chance of inclusion in a sample. Randomization is necessary to deal with individual differences. Replicate - Replicates are individuals or groups that are exposed to the same conditions in an experiment, including the same level of the independent variable. It is necessary to have replicates to prove a relationship between independent and dependent variables. Sampling - The sample is the portion of a population examined in your study. If the population is divided into sub-groups then it is important to have the correct relative ratio of the sub-groups in the sample. Theory - An explanation of existing data that can explain present data as well as predict future data. Treatment - A treatment is a factor that may affect the outcome of an experiment.

• Norman Herr, Ph.D.