why are positive and negative controls important in an experiment

Positive Control vs Negative Control: Differences & Examples

Chris Drew (PhD)

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A positive control is designed to confirm a known response in an experimental design , while a negative control ensures there’s no effect, serving as a baseline for comparison.

The two terms are defined as below:

• Positive control refers to a group in an experiment that receives a procedure or treatment known to produce a positive result. It serves the purpose of affirming the experiment’s capability to produce a positive outcome.
• Negative control refers to a group that does not receive the procedure or treatment and is expected not to yield a positive result. Its role is to ensure that a positive result in the experiment is due to the treatment or procedure.

The experimental group is then compared to these control groups, which can help demonstrate efficacy of the experimental treatment in comparison to the positive and negative controls.

Positive Control vs Negative Control: Key Terms

Control groups.

A control group serves as a benchmark in an experiment. Typically, it is a subset of participants, subjects, or samples that do not receive the experimental treatment (as in negative control).

This could mean assigning a placebo to a human subject or leaving a sample unaltered in chemical experiments. By comparing the results obtained from the experimental group to the control, you can ascertain whether any differences are due to the treatment or random variability.

A well-configured experimental control is critical for drawing valid conclusions from an experiment. Correct use of control groups permits specificity of findings, ensuring the integrity of experimental data.

See More: Control Variables Examples

The Negative Control

Negative control is a group or condition in an experiment that ought to show no effect from the treatment.

It is useful in ensuring that the outcome isn’t accidental or influenced by an external cause. Imagine a medical test, for instance. You use distilled water, anticipating no reaction, as a negative control.

If a significant result occurs, it warns you of a possible contamination or malfunction during the testing. Failure of negative controls to stay ‘negative’ risks misinterpretation of the experiment’s result, and could undermine the validity of the findings.

The Positive Control

A positive control, on the other hand, affirms an experiment’s functionality by demonstrating a known reaction.

This might be a group or condition where the expected output is known to occur, which you include to ensure that the experiment can produce positive results when they are present. For instance, in testing an antibiotic, a well-known pathogen, susceptible to the medicine, could be the positive control.

Positive controls affirm that under appropriate conditions your experiment can produce a result. Without this reference, experiments could fail to detect true positive results, leading to false negatives. These two controls, used judiciously, are backbones of effective experimental practice.

Experimental Groups

Experimental groups are primarily characterized by their exposure to the examined variable.

That is, these are the test subjects that receive the treatment or intervention under investigation. The performance of the experimental group is then compared against the well-established markers – our positive and negative controls.

For example, an experimental group may consist of rats undergoing a pharmaceutical testing regime, or students learning under a new educational method. Fundamentally, this unit bears the brunt of the investigation and their response powers the outcomes.

However, without positive and negative controls, gauging the results of the experimental group could become erratic. Both control groups exist to highlight what outcomes are expected with and without the application of the variable in question. By comparing results, a clearer connection between the experiment variables and the observed changes surfaces, creating robust and indicative scientific conclusions.

Positive and Negative Control Examples

1. a comparative study of old and new pesticides’ effectiveness.

This hypothetical study aims to evaluate the effectiveness of a new pesticide by comparing its pest-killing potential with old pesticides and an untreated set. The investigation involves three groups: an untouched space (negative control), another treated with an established pesticide believed to kill pests (positive control), and a third area sprayed with the new pesticide (experimental group).

• Negative Control: This group consists of a plot of land infested by pests and not subjected to any pesticide treatment. It acts as the negative control. You expect no decline in pest populations in this area. Any unexpected decrease could signal external influences (i.e. confounding variables ) on the pests unrelated to pesticides, affecting the experiment’s validity.
• Positive Control: Another similar plot, this time treated with a well-established pesticide known to reduce pest populations, constitutes the positive control. A significant reduction in pests in this area would affirm that the experimental conditions are conducive to detect pest-killing effects when a pesticide is applied.
• Experimental Group: This group consists of the third plot impregnated with the new pesticide. Carefully monitoring the pest level in this research area against the backdrop of the control groups will reveal whether the new pesticide is effective or not. Through comparison with the other groups, any difference observed can be attributed to the new pesticide.

2. Evaluating the Effectiveness of a Newly Developed Weight Loss Pill

In this hypothetical study, the effectiveness of a newly formulated weight loss pill is scrutinized. The study involves three groups: a negative control group given a placebo with no weight-reducing effect, a positive control group provided with an approved weight loss pill known to cause a decrease in weight, and an experimental group given the newly developed pill.

• Negative Control: The negative control is comprised of participants who receive a placebo with no known weight loss effect. A significant reduction in weight in this group would indicate confounding factors such as dietary changes or increased physical activity, which may invalidate the study’s results.
• Positive Control: Participants in the positive control group receive an FDA-approved weight loss pill, anticipated to induce weight loss. The success of this control would prove that the experiment conditions are apt to detect the effects of weight loss pills.
• Experimental Group: This group contains individuals receiving the newly developed weight loss pill. Comparing the weight change in this group against both the positive and negative control, any difference observed would offer evidence about the effectiveness of the new pill.

3. Testing the Efficiency of a New Solar Panel Design

This hypothetical study focuses on assessing the efficiency of a new solar panel design. The study involves three sets of panels: a set that is shaded to yield no solar energy (negative control), a set with traditional solar panels that are known to produce an expected level of solar energy (positive control), and a set fitted with the new solar panel design (experimental group).

• Negative Control: The negative control involves a set of solar panels that are deliberately shaded, thus expecting no solar energy output. Any unexpected energy output from this group could point towards measurement errors, needed to be rectified for a valid experiment.
• Positive Control: The positive control set up involves traditional solar panels known to produce a specific amount of energy. If these panels produce the expected energy, it validates that the experiment conditions are capable of measuring solar energy effectively.
• Experimental Group: The experimental group features the new solar panel design. By comparing the energy output from this group against both the controls, any significant output variation would indicate the efficiency of the new design.

4. Investigating the Efficacy of a New Fertilizer on Plant Growth

This hypothetical study investigates the efficacy of a newly formulated fertilizer on plant growth. The study involves three sets of plants: a set without any fertilizer (negative control), a set treated with an established fertilizer known to promote plant growth (positive control), and a third set fed with the new fertilizer (experimental group).

• Negative Control: The negative control involves a set of plants not receiving any fertilizer. Lack of significant growth in this group will confirm that any observed growth in other groups is due to the applied fertilizer rather than other uncontrolled factors.
• Positive Control: The positive control involves another set of plants treated with a well-known fertilizer, expected to promote plant growth. Adequate growth in these plants will validate that the experimental conditions are suitable to detect the influence of a good fertilizer on plant growth.
• Experimental Group: The experimental group consists of the plants subjected to the newly formulated fertilizer. Investigating the growth in this group against the growth in the control groups will provide ascertained evidence whether the new fertilizer is efficient or not.

5. Evaluating the Impact of a New Teaching Method on Student Performance

This hypothetical study aims to evaluate the impact of a new teaching method on students’ performance. This study involves three groups, a group of students taught through traditional methods (negative control), another group taught through an established effective teaching strategy (positive control), and one more group of students taught through the new teaching method (experimental group).

• Negative Control: The negative control comprises students taught by standard teaching methods, where you expect satisfactory but not top-performing results. Any unexpected high results in this group could signal external factors such as private tutoring or independent study, which in turn may distort the experimental outcome.
• Positive Control: The positive control consists of students taught by a known efficient teaching strategy. High performance in this group would prove that the experimental conditions are competent to detect the efficiency of a teaching method.
• Experimental Group: This group consists of students receiving instruction via the new teaching method. By analyzing their performance against both control groups, any difference in results could be attributed to the new teaching method, determining its efficacy.

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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

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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Positive and Negative Controls

This is part of the NSW HSC science curriculum part of the Working Scientifically skills.

Positive and Negative Controls Explained

Introduction to Controls in Scientific Experiments

Controls are standard benchmarks used in experiments to ensure that the results are due to the factor being tested and not some external influence. There are two main types of controls: positive and negative. Controls play an important part in ensuring that the experimental results are valid.

Note that controls and controlled variables refer to different aspects of experiments.

Positive Controls

Positive controls are used in experiments to show what a positive result looks like. They ensure that the testing procedure is capable of producing results when the expected outcome is present.  They involve using a material or condition known to produce a positive result.

Positive controls confirm that the experimental setup can detect positive results and that all reagents and instruments are functioning correctly and as intended.

Negative Controls

Negative controls, on the other hand, are used to ensure that no change is observed when a change is not expected. They help confirm that any positive result in the experiment is truly due to the test condition and not due to external factors.

Why Do We Use Positive and Negative Controls?

Rule Out False Positives : Negative controls help in ruling out the possibility that external factors are causing the observed effect.

No Expected Outcome : These controls involve using a material or condition known not to produce the effect being tested.

Validity and Reliability : Positive and negative controls are crucial for establishing the validity and reliability of an experiment. They provide a way of checking whether the experimental method actually tests the what it's supposed to test, and a basis for comparison to the experimental group.

Error Identification : Controls can help identify errors in the experimental setup or procedure, ensuring that the results of an experiment are due to the variable being tested.

Interpretation of Results : Understanding what constitutes normal variation in an experiment is essential for accurately interpreting results.

Example of Controls in Chemistry

Experiment : Testing the Presence of Vitamin C in Fruit Juice

Aim:  To determine whether a particular fruit juice contains Vitamin C.

Positive Control : For this experiment, a known Vitamin C solution can be used. This solution should react positively with the testing reagent (like DCPIP, which changes colour in the presence of Vitamin C) to show that the test can indeed identify Vitamin C when it is present.

Negative Control : Distilled water serves as an effective negative control. It does not contain Vitamin C and should not react with the testing reagent. Any change in the negative control indicates contamination or an error in the experimental procedure.

Example of Controls in Physics

Experiment: Investigating Newton's Second Law of Motion

Aim : To verify Newton's Second Law of Motion, which states that the acceleration of an object is directly proportional to the net force acting on it and inversely proportional to its mass (`F = ma`).

Experimental Setup:

Students use a dynamic cart on a track, a set of known masses, a pulley system, and a force sensor or photogate timer to measure acceleration.

A photogate measures the velocity of cart by using how long the light beam within the gate has been obstructed by the opaque band mounted on the moving cart. By using the velocities measured by the two photogates and the time difference between the two, acceleration of the cart can be determined.

You can use the following simulation to familiarise with photogates.

• Positive Control: To ensure that the experimental setup e.g. photogate can correctly measure acceleration, use a cart with known mass and a predetermined force (e.g. weight force of a where `F = ma` can be accurately calculated. This setup should produce a predictable acceleration. When the experiment is conducted with these known values, the measured acceleration should closely match the theoretical acceleration calculated. This confirms that the equipment (force sensor, photogate timer, etc.) is functioning correctly and the experimental procedure is valid.
• Negative Control: To ensure that the measured acceleration is solely due to the applied force and not any other factors like friction or air resistance, conduct an experiment with no external force applied (other than the minimal force to overcome static friction). This can be done by using a dynamic cart on a level track without adding any additional weights or forces. The cart should exhibit minimal to no acceleration, indicating that any acceleration measured in the main experiment is due to the applied force and not inherent biases or errors in the setup.

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Control Group Definition and Examples

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

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

Control Group vs Experimental Group

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

Control Group vs Control Variable

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

Types of Control Groups

There are different types of control groups:

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

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

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

Positive and Negative Controls

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

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

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

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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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Positive and Negative Controls

To ensure the accuracy and reliability of experimental results, it is crucial to include both positive and negative controls in the experimental design. These controls help to validate the experiment by demonstrating that the setup works as intended and that the results are due to the experimental conditions rather than other variables.

What is a Positive Control?

A positive control is a sample that is treated in a way that is known to produce a positive result. This control confirms that the experiment is capable of producing results under the experimental conditions. By including a positive control, scientists can verify that the experimental procedure is functioning as expected.

For example, in a Western blot experiment designed to detect a specific protein, a cell lysate known to express the protein of interest would be used as a positive control. The presence of a band corresponding to the protein on the blot demonstrates that the Western blot procedure is working correctly, the antibodies are binding as expected, and the detection reagents are functional.

What is a Negative Control?

A negative control , on the other hand, is a sample that is treated the same as all other experimental samples but is not expected to produce a change. This control helps to demonstrate that any changes observed in the experiment are indeed due to the experimental variable and not because of other factors.

In the context of the Western blot example, a negative control might be a cell lysate that does not express the protein of interest. If no band appears for this sample, it confirms that the detected bands in the experimental samples are specific to the protein of interest and not due to nonspecific binding or other experimental artifacts.

Loading Control Antibodies

Loading control antibodies mostly recognize housekeeping proteins in cells used in a scientific experiment and allow the verification of equal protein loading between samples. Ideal loading controls are expressed constitutively and at high levels with low variability between cell lines and experimental conditions.

Loading controls are essential for the interpretation of assays. In Western blot assays, the loading control should be at a different molecular weight than the protein of interest, as this allows the protein to be visually distinguishable. Loading control antibodies not only allow the verification of equal protein loading between samples in Western blot assays, but they also allow for identification of certain cell compartmentalization or cellular localization in immunofluorescence microscopy (IF) and immunohistochemistry (IHC). Rockland’s loading control antibodies are suitable in assays including ELISA, FLISA, Western blot, IF, and IHC.

Western blot of pERK1/2, ERK1/2 . α-tubulin is used as a control to illustrate uniform protein loading.

Featured Loading Control Antibodies

Alpha-Tubulin Antibody

Beta Actin Antibody

GAPDH Antibody

Control cell lysates and nuclear extracts.

Rockland offers control cell lysates and nuclear extracts for use on SDS-PAGE as standalone samples or in combination with antibodies in Western blotting experiments. Our ready-to-use whole-cell lysates and nuclear extracts are derived from cell lines or tissues using highly advanced extraction protocols to ensure high quality, protein integrity, and lot-to-lot reproducibility.

Lysates are generated from either whole cells, which contain cell membrane, cytoplasmic, and nuclear proteins, or nuclear extracts, which are predominantly proteins that originate in the nucleus. Control lysates may be from cells that are stimulated with insulin, doxorubicin, etoposide, nocodozole, TNFa, or EGF. Lysates are also available from normal animal tissue derived from primary organs such as liver, heart, and brain. Additionally, Rockland offers a variety of lysates that contain over-expressed proteins (tagged and untagged) that can serve as positive controls for antibody reactivity. All extracts are tested by SDS-PAGE using 4–20% gradient gels and immunoblot analysis using antibodies to key cell signaling components to confirm the presence of both high molecular weight and low molecular weight proteins.

Featured Control Cell Lysates

Human Foreskin Fibroblast Whole Cell Lysate

A431 Whole Cell Lysate EGF Stimulated

E.coli HCP Control

12 Epitope Tag Control Lysate (GST)

Purified proteins.

Purified proteins or peptides are ideal as controls in flow cytometry, Western blot, and ELISA. Proteins can be used as loading controls in Western blot experiments or as titration agents in ELISA experiments. Rockland produces purified immunoglobulin proteins from a variety of species, often available by immunoglobulin class or as fragments of immunoglobulins. Peptides can be used to do competition assays or to be used in peptide arrays.

Featured Control Proteins

Hamster IgG

Low endotoxin controls.

Low endotoxin control proteins are IgG preparations of control serum purified by protein A chromatography using a low endotoxin methodology. These controls are ideal in biological assays like neutralization experiments, ELISA, flow cytometry, and other assays. For neutralization assays, where antibodies to cytokines, interleukins, infectious disease, and growth factors may be used to block bioactivity, our low endotoxin IgG serve as ideal control proteins. Rockland offers purified, low-endotoxin mouse and rabbit IgG.

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Positive and Negative Control in Microbiology

Positive and Negative Control, Microbiology, bacteriology, virology, mycology, Positive vs Negative Control, different between Positive and Negative Control.

This discussion provides insights into positive controls, where an testing sample is tested, and negative controls, which aim to detect possible procedural errors.

Table of Contents

Basics of Positive Control in Microbiology

Positive control in microbiology: the basics, importance of positive control.

Positive controls offer a validation mechanism for microbiological research. They ensure that the experimental setup functions as intended and that any negative results are exact and not because the experiment itself failed. Because it contains known organisms that can successfully be grown, a positive control proves that the lab conditions, chemicals, and methods used in the experiment are effective. Therefore, should an experimental test fail to produce expected results, scientists will know to question the experimental procedures put in place.

Positive Control in Different Microbiology Areas

In various microbiology areas such as bacteriology , virology , and mycology , usage of positive controls is prevalent.

For example, in bacteriology, E. coli is often introduced as a positive control when testing for coliform bacteria in water systems. Because E. coli is easily identifiable and frequently present in contaminated water, it assists scientists in confirming that their methods for cultural, identification, and counting are adequate.

In the field of mycology, which deals with fungi, Aspergillus or Penicillium might be used as positive controls depending on the context of the experiment. The results from this control assist in validating the growth conditions and reagents employed in the experiment.

Negative Control in Microbiology: The Basics

By contrast, a negative control in microbiological tests is a parallel test setup using conditions known to give no response. This test is crucial because it demonstrates the absence of non-specific effects and validates the specificity of the results.

Similarly, in a polymerase chain reaction (PCR) testing, a negative control, such as sterile water, is included with each batch of reactions. The absence of amplification (no bands in gel electrophoresis) validates that there are no non-specific amplifications due to contaminants.

Hence, the negative control contributes to ensuring the reliability and the specificity of the experiment.

Understanding Negative Control in Microbiology

An introduction to negative control in microbiology.

The concept of negative control in microbiology forms a key part of research design, serving as a ‘benchmark’ or ‘norm’ for contrasting and evaluating the results of the experiment. Essentially, a negative control is a subset in a particular study not expected to yield a significant outcome, which verifies that the observed effects were not caused by the experimental process itself.

Significance of Negative Control in Microbiology Experiments

Functions of negative control: eliminating false positives and errors, examples of negative control in several microbiology fields.

In various fields of microbiology, negative controls are frequently used. For instance, in antibiotic susceptibility testing, a bacterial sample is cultured in the absence of antibiotics. If the bacteria still do not grow, it signifies a problem with the culture conditions or the bacteria itself, not necessarily the antibiotics’ effectiveness.

Understanding Positive Control in Microbiology

Interaction of positive and negative controls in microbiology.

The interplay between negative and positive controls is crucial for accurate, credible experimental outcomes. While negative controls help rule out extraneous effects or false positives, positive controls ensure the experiment is functioning as intended.

Essential Role of Controls in Microbiology Experiments

Difference between positive and negative controls, positive vs negative controls.

Positive and negative controls are cornerstones in microbiology experiments, functioning as instrumental tools in corroborating the dependability of the obtained results. While they follow a symbiotic relationship in the context of maintaining standards, their individual functions differ significantly.

Exploring Positive Controls

Delving into negative controls.

Conversely, a negative control does not involve any change. Typically, this sample encompasses a test environment, such as a microbiological growth medium, which does not contain any targeted bacteria or associated treatment. Negative controls play an integral role in confirming that any observed deviations originate from experimental procedures and not from extraneous or non-intentional factors including contamination.

Significance of Positive and Negative Controls

Together, positive and negative controls help develop faith in experimental outcomes by affirming the experiment’s validity. They also aid in capturing potential error sources.

In contrast, negative controls point out if non-intentional effects are introduced into the experimental setup. Discrepancies in the negative control signal towards possible contamination or equipment malfunction.

Appropriate Usage of Positive and Negative Controls

Positive and negative controls are employed throughout different stages of a microbiology experiment.

While the nature of these controls varies, their importance in assuring reliable, reproducible results that can be accepted by the scientific community remains constant regardless of the experimental setup.

Making easier to understand the concepts of positive and negative controls essentially boils down to their core roles in scientific experiments. Positive controls serve as a benchmark to confirm that the experimental setup is working as intended, while negative controls act as the litmus test for accidental errors, preventing the intrusion of false positives. Across bacteriology, virology, mycology, and more, these controls ensure that the scientific findings we base our decisions on are accurate and reliable.

FAQ – Positive and Negative Control in Microbiology

What is a positive control in microbiology, what is negative control test in microbiology.

A negative control test is an experiment in which the microbiologist knows that there will be a negative outcome. This is done to ensure that the test is not contaminated and that the results are accurate. For example, in a test for the presence of bacteria, a negative control would be a sterile solution. If the test detects bacteria in the negative control, then it is likely that the test is contaminated.

What is a positive and negative control example?

A positive control is an experiment that is expected to produce a positive outcome. For example, a positive control for a test for the presence of bacteria would be a known bacteria culture. A negative control is an experiment that is expected to produce a negative outcome. For example, a negative control for a test for the presence of bacteria would be a sterile solution.

Why use negative control in microbiology?

What is the difference between positive and negative control groups.

Positive control groups are exposed to a treatment that is known to produce a specific outcome. Negative control groups are not exposed to any treatment and are expected to have no change.

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What Is a Control Group?

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A control group in a scientific experiment is a group separated from the rest of the experiment, where the independent variable being tested cannot influence the results. This isolates the independent variable's effects on the experiment and can help rule out alternative explanations of the experimental results.

A control group definition can also be separated into two other types: positive or negative.

Positive control groups are groups where the conditions of the experiment are set to guarantee a positive result. A positive control group can show the experiment is functioning properly as planned.

Negative control groups are groups where the conditions of the experiment are set to cause a negative outcome.

Control groups are not necessary for all scientific experiments. Controls are extremely useful when the experimental conditions are complex and difficult to isolate.

Example of a Negative Control Group

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

Several experimental groups could differ in the fertilizer concentration, application method, etc. The null hypothesis would be that the fertilizer does not affect plant growth. Then, if a difference is seen in the growth rate or the height of plants over time, a strong correlation between fertilizer and growth would be established. Note the fertilizer could have a negative impact on growth rather than positive. Or, for some reason, the plants might not grow at all. The negative control group helps establish the experimental variable is the cause of atypical growth rather than some other (possibly unforeseen) variable.

Example of a Positive Control Group

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

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

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Chapter 12: Statistics in Practice

Back to chapter, controls in experiments, previous video 12.6: crossover experiments, next video 12.10: clinical trials.

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

Home » Science » Chemistry » Biochemistry » Difference Between Positive and Negative Control

Difference Between Positive and Negative Control

Main difference – positive vs negative control.

Scientific control is a methodology that tests integrity in experiments by isolating variables as dictated by the scientific method in order to make a conclusion about such variables. It can be defined as an experiment that is designed to minimize the effect of variables other than the independent variables. (The things that are changing in an experiment are called variables). An experiment can be positively or negatively controlled. The main difference between positive and negative control is that positive control gives a response to the experiment whereas negative control does not give any response.

Key Areas Covered

1. What is Positive Control      – Definition, Process, Uses 2. What is Negative Control      – Definition, Process 3. What is the Difference Between Positive and Negative Control     – Comparison of Key Differences

Key Terms: Assay, Control, Experiment, Negative Control, Positive Control

What is Positive Control

A positive control is an experimental control that gives a positive result at the end of the experiment. This type of test always gives the result as a “yes”. It is a good indication to know if the test works. Hence, positive controls are used to evaluate the validity of a test.

The positive control is not exposed to the experimental test; it is done parallel to it. The positive control is used to get the expected result. This positive result ensures the success of the test. Once the positive result is given, the test can be used for the experimental treatment. If the positive control does not give the expected result, it should be done again and again (by varying different parameters) until a positive result is given.

Figure 1: ELISA experiment – An Enzyme Assy

There are many applications of positive control in biochemical experiments.

• To detect a disease
• To observe the growth of microorganisms
• To measure the amount of enzymes present after an enzyme assay is done (in positive control, the amount of enzyme after the purification should be a known amount)

What is Negative Control

A negative control is an experimental control that does not give a response to the test. The negative control is also not exposed to the experimental test directly. It is done parallel to the experiment as a control experiment.

The negative control is used to confirm that there is no response to the reagent or the microorganism (or any other parameter) used in the test. In order to get a good result from the negative control, one should ensure that there is no net response to the test. Hence, negative controls are helpful in identifying outside influences on the experiment. For example, the effect of contaminants on an experiment can be indicated.

Positive Control: A positive control is an experimental control that gives a positive result at the end of the experiment.

Negative Control: A negative control is an experimental control that does not give a response to the test.

Positive Control: Positive control gives positive result

Negative Control: Negative control gives a negative result.

Positive Control: Positive control gives a response to the experiment.

Negative Control: Negative control does not give any response.

Positive Control: Positive control ensures the success of the test.

Negative Control: Negative control is used to ensure that there is no response to the test.

Positive Control: Positive control is used to test the validity of an experiment.

Negative Control: Negative control is used to identify the influence of external factors on the test.

Positive control and negative control are two types of tests that give completely opposite responses in an experiment. The main difference between positive and negative control is that positive control gives a response to the experiment whereas negative control does not give any response.

 1. “Scientific Control.” The Titi Tudorancea Bulletin, Available here . 2. “Scientific control.” Wikipedia, Wikimedia Foundation, 24 Jan. 2018, Available here .

About the Author: Madhusha

Madhusha is a BSc (Hons) graduate in the field of Biological Sciences and is currently pursuing for her Masters in Industrial and Environmental Chemistry. Her interest areas for writing and research include Biochemistry and Environmental Chemistry.

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Control Group vs Experimental Group

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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.

Positive and negative controls

In chemistry, controls are a way to validate the results of your experiment. A positive control should show a positive result in a test, whereas a negative control should show a negative result. If the result is not as expected, you cannot trust that the test was performed correctly for your actual experiment.

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Why is it important to include both positive and negative controls in your IF experiment?

• Efficacy and potency of treatment-induced modulation
• Secondary antibody quality
• That processing steps were executed effectively
• That buffers are in good working order
• Secondary antibody alone to assess its contribution to overall signal
• Isotype control to determine if anything in the sample causes non-specific primary antibody binding 
• Cells/tissue only to set a baseline for sample autofluorescence
• A cell line or tissue with known negative-expression for the target of interest

Every IF staining experiment we perform   at CST includes the following elements:

1. S6 Ribosomal Protein (5G10) Rabbit mAb #2217 as a robust readout for both quality of fixation/processing and secondary antibody health. 2. Secondary antibody only and cells/tissues alone controls 3. A treatment-appropriate control, when applicable.

Last updated: September 12, 2024

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Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies

Marc lipsitch.

1 Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115

2 Department of Immunology and Infectious Diseases, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115

3 Center for Communicable Disease Dynamics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115

Eric Tchetgen Tchetgen

4 Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115

5 Division of Global Health Equity, Brigham and Women’s Hospital, Boston MA 02115

Associated Data

Non-causal associations between exposures and outcomes are a threat to validity of causal inference in observational studies. Many techniques have been developed for study design and analysis to identify and eliminate such errors. Such problems are not expected to compromise experimental studies, where careful standardization of conditions (for laboratory work) and randomization (for population studies) should, if applied properly, eliminate most such non-causal associations. We argue, however, that a routine precaution taken in the design of biological laboratory experiments—the use of “negative controls”—is designed to detect both suspected and unsuspected sources of spurious causal inference. In epidemiology, analogous negative controls help to identify and resolve confounding as well as other sources of error, including recall bias or analytic flaws. We distinguish two types of negative controls (exposure controls and outcome controls), describe examples of each type from the epidemiologic literature, and identify the conditions for the use of such negative controls to detect confounding. We conclude that negative controls should be more commonly employed in observational studies, and that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies.

Epidemiologists seek to distinguish the causal effect of exposure A on outcome Y from associations due to other mechanisms ( Figure 1 ). Non-causal associations may be classified into three categories (in addition to chance) 1 : mismeasurement (eg, recall bias), confounding, and biased selection of individuals into the analysis.

Causal diagram for the effect of an exposure of interest (A) on an outcome of interest (Y), with confounders L (assumed measured) and U (assumed uncontrolled) that cause both A and Y. The dashed line between L and U indicates that either may cause the other, and they may share common causes.

In experimental biology, the manipulation of experimental conditions prevents many of the noncausal associations that arise in observational studies. Nonetheless, experimental biologists routinely question whether they have correctly inferred causal relationships from the results of their experiments. Biologists employ “negative controls” as a means of ruling out possible noncausal interpretations of their results. We describe the use of negative controls in experiments, highlight some examples of their use in epidemiologic studies, and define the conditions under which negative controls can detect confounding in epidemiologic studies. Although the particular threats to causal inference are different in experimental and observational sciences, the use of negative controls is a valuable means of identifying noncausal associations and can complement other epidemiologic methods for improving causal inference.

Experimental biology: threats to causal inference and the use of negative controls

One might imagine that the experimental method would circumvent most threats to the validity of causal inference that occur in observational studies. For example, consider the hypothesis that a particular cytokine—a chemical involved in signaling in the immune system—enhances the killing of a species of bacteria by neutrophils, a class of white blood cells. 2 An experiment is devised in which neutrophils, bacteria, and growth medium are mixed together. In condition 1, the cytokine is added, and in condition 2, some inert substance such as saline solution is added. After incubation, the bacteria are enumerated and the number of live bacteria compared between conditions 1 and 2.

If the investigator finds fewer live bacteria in condition 1 than in condition 2, the finding is consistent with the hypothesis that the cytokine enhanced neutrophil-mediated killing. Nonetheless, concern remains that something other than cytokine-aided, neutrophil-mediated killing may be responsible. For example, perhaps there is a contaminant in the cytokine preparation that directly kills bacteria, or perhaps the cytokine itself kills bacteria, or perhaps some other unintended difference between the treated and untreated conditions (e.g., temperature or pH) caused the differential survival of the bacteria.

Each of these unintended differences is broadly similar to a confounder – a characteristic associated with the exposure (presence or absence of the cytokine) and causes the outcome (differences in bacterial counts), thereby causing a spurious association between the presence of the cytokine and differences in bacterial counts.

Experimental biologists address such concerns in two ways. The first is to attempt to eliminate unwanted differences between the compared groups (in the design) and to measure and account for any unavoidable differences (in the analysis). For example, a researcher would make all conditions (dilution protocols, incubators, etc.) identical between the two conditions except for the variable of interest (i.e. the presence/absence of the cytokine). Replication of the experiment reduces the likelihood that some chance factor was systematically different between the two experimental arms. Sometimes experimental variation nonetheless remains. When experimental variation cannot be eliminated by these approaches, experimentalists may control for this variation by matching or statistical adjustment for the day on which an assay was performed. In experimental studies of population health outcomes (clinical trials), analogous precautions include randomization (to assure an expectation of baseline exchangeability between groups) 3 , use of multiple individuals in each treatment group (replication), and analytic adjustment for measured confounders.

The second general approach is to perform negative controls: to repeat the experiment under conditions in which it is expected to produce a null result and verify that it does indeed produce a null result. Several strategies are employed to design negative controls, 2 such as:

• Leave out an essential ingredient. In the absence of neutrophils, there should be the same number of bacteria with or without the cytokine; if a contaminant (or the cytokine itself) is killing bacteria without involving neutrophils, this negative control should produce fewer bacteria with the cytokine than without.
• Inactivate the (hypothesized) active ingredient. Specific antibodies that neutralize the cytokine (but would have no effect on a contaminant) can be added to the preparation; killing should not occur if the cytokine is responsible for the effect.
• Check for an effect that would be impossible by the hypothesized mechanism. Suppose there were a species of bacteria that was completely impervious to the actions of neutrophils. The experiment could be repeated with this species, rather than the species of interest, to confirm there is no difference between condition 1 and condition 2. This would help to rule out the possibility of some non-neutrophil-mediated effect of the cytokine preparation on bacteria.

As with the list of non-causal explanations for an experimental result, the list of possible negative controls is almost endless, and judgment is required to assess how many such non-causal explanations are plausible and which negative controls are of greatest value in ruling out key threats to valid inference. Peer reviewers of biological experiments usually require some negative controls to validate experimental results.

Examples of the use of negative controls in epidemiology

In an epidemiologic study to assess whether an association between a risk factor A and an outcome Y is likely to be causal, it is common to address the possibility of confounding by measured variables L by adjusting for them, using such techniques as restriction, stratification, multivariate modeling, matching, inverse-probability weighting, or g-estimation. 4

Epidemiologists also sometimes use negative controls to detect confounding and other sources of incorrect causal inference. This approach has been elegantly applied to the debate over vaccination of the elderly and effects on “pneumonia or influenza hospitalizationpneumonia/influenza on all-cause mortality. Observational studies in elderly persons have shown that vaccination against influenza is associated with a remarkably large reduction in one’s risk of pneumonia/influenza hospitalization and also in one’s risk of all-cause mortality in the following season, after adjustment for measured covariates that indicate health status. 5 However, older age is associated with a less robust immune response to influenza vaccination, and ecological data suggest that the benefits measured in observational studies far exceed the corresponding benefits expected at the population level when influenza vaccination rates have increased among the elderly. 6 Importantly, both outcomes are nonspecific, in the sense that they have unknown and time-varying contributions from influenza. This is obviously true for all-cause mortality, and it is also true for pneumonia/influenza hospitalization, since the cause of respiratory infection is often not ascertained, and many pneumonia cases are caused by agents other than influenza. The large degree of protection against these outcomes observed in individual level studies, combined with the lack of measurable vaccine effect in ecological studies, have led to a suspicion that uncontrolled confounding has exaggerated the impact of influenza vaccination on mortality and on pneumonia/influenza hospitalization in the elderly. 6 – 8

To test this hypothesis, Jackson et al 7 reproduced earlier estimates of the protective effect of influenza vaccination, but then repeated the analysis for two sets of negative control outcomes, and showed that the protective effect was observed even in circumstances where the vaccine could not have caused the protection. For the first negative control outcome, the authors 7 used the fact that vaccination often begins in autumn, while influenza transmission is often minimal until winter. Thus they could assess the risk of pneumonia/influenza hospitalization and all-cause mortality among vaccinated vs. unvaccinated persons both before, during and after influenza season. The only biologically plausible mechanism by which influenza vaccine could protect against mortality or pneumonia/influenza hospitalization is by preventing influenza or its consequences; therefore, Jackson and colleagues 7 reasoned that if the effect measured in prior studies were causal, it should be most prominent during influenza season. If instead it were due to confounding, then the protective effect should be observable immediately after vaccination but before influenza season. In a cohort study analyzed with a Cox proportional hazards model, despite efforts to control for confounding, they observed that the protective effect was actually greatest before, intermediate during, and least after influenza season. They concluded that this is evidence that confounding, rather than protection against influenza, accounts for a substantial part of the observed “protection.” The use of this negative-control outcome approach is formally similar to the “leave-out-an-essential-ingredient” control described above, as influenza is essential in the proposed causal pathway.

Second, Jackson et al. 7 postulated that the protective effects of influenza vaccination, if real, should be limited to outcomes plausibly linked to influenza. In contrast, if the relationship were due to an uncontrolled confounder, then the same “protection” might be observed for irrelevant outcomes. They repeated their analysis, but substituted hospitalization for injury or trauma as the endpoint. They found that influenza vaccination was also “protective” against injury or trauma hospitalization. This, too, was interpreted as evidence that some of the protection observed for pneumonia/influenza hospitalization or mortality was due to inadequately controlled confounding. This second negative control outcome is formally similar to the “check-for-an-effect-impossible-by-the-hypothesized-mechanism” approach described above.

Epidemiologists also sometimes use negative control exposures to examine whether observed associations are causal. An example is the inclusion in questionnaires of irrelevant variables, sometimes called “probe variables,” to assess if recall bias may be responsible for an observed association between a self-reported exposure and an outcome. A recent study 9 tested the association between multiple sclerosis (MS) and a variety of common childhood infections assessed by self-report. The investigators found statistically significant positive associations of MS with a recalled history of five different viral infections. Suspecting that cases may recall prior medical events more often or with more certainty than controls, the investigators’ questionnaire also included several childhood medical events not plausibly associated with MS, such as broken limbs, tonsillectomy, and concussions. In the absence of a causal association, any measured association with these probe variables would suggest recall bias for the variables of interest. The authors found that the magnitude of association with these irrelevant exposures was comparable to the magnitude observed for each of the self-reported infections except one (infectious mononucleosis) that had a much stronger association. They concluded that, after accounting for recall bias, only infectious mononucleosis showed a specific association with MS.

Another application of negative controls has been to expose “immortal time bias,” a form of selection bias that produces spurious associations between observed variables. Suissa and Ernst 10 suspected that the reported benefits of nasal corticosteroids in preventing asthma resulted from this form of bias, in which exposed persons are credited with time at risk during which the event cannot occur, and thus exposed persons have an artificially low event rate. Inclusion of the “immortal” time is dependent on both being exposed during that time and on not having the outcome during that time 10 ; hence a (negative) association is induced between exposure and outcome. To demonstrate such bias, the authors repeated prior analyses but restricted the exposed class to persons with a single annual dose of corticosteroids– a dose far too low to have plausible biological effect (i.e. a negative control exposure). They found that even this very modest exposure was associated with substantial protection against asthma, suggesting that the previous analytic approach was inappropriate. In this case, the investigators already suspected what form of bias was operating, and used the analysis to prove their point. In principle, the original investigators could have done such an analysis to test for bias.

Choice of negative controls to detect confounding in epidemiology

Negative controls have been used to detect confounding (the influenza vaccine example 7 ), recall bias, (the MS example 9 ), and selection bias (the nasal corticosteroid example 10 ). Furthermore, it may be possible to specify how negative controls should be designed to aid in detecting biased causal inferences resulting from each of these mechanisms, and also perhaps to detect other forms of analytical errors. In this section, we focus on the conditions under which negative controls in epidemiology can detect confounding. 1

The essential purpose of a negative control is to reproduce a condition that cannot involve the hypothesized causal mechanism, but is very likely to involve the same sources of bias that may have been present in the original association. If a contaminant (source of bias) were responsible for the effect of the cytokine on bacteria, it should have its effect even when the hypothesized mechanism of the effect (through neutrophils) is prevented through neutralization of the cytokine or through omission of neutrophils from the experiment. If an uncontrolled confounder (general good health or healthful practices) is responsible for the protection observed from influenza vaccine against mortality or pneumonia/influenza hospitalization, the same confounder might be associated with other outcomes that are not plausibly prevented by influenza vaccination.

This description suggests a general principle for the selection of negative controls to detect residual confounding. Ideally, a negative control outcome (N) should be an outcome such that the set of common causes of exposure A and outcome Y should be as identical as possible to the set of common causes of A and N ( Figure 2 ). To the extent that the set of unobserved common causes of A and Y overlaps with the set of unobserved common causes (U) of A and N, we call the negative control outcome N “U-comparable” to Y. If N and Y are U-comparable outcomes (i.e. with an identical set of common causes that are associated with A), and assuming that N is not caused by A, an association A-N when analyzed according to the same procedure used to analyze A-Y would indicate bias in the association A-Y. If N and Y are perfectly U-comparable and N is not caused by A, then a null finding of A-N implies that the A-Y association is not likely biased by the pathways examined through this negative control.

Causal diagram showing an ideal negative control outcome N for use in evaluating studies of the causal relationship between exposure A and outcome Y. N should ideally have the same incoming arrows as Y, except that A does not cause N; to the extent this criterion is met, N is called U-comparable to Y.

Negative control outcomes in practice will be only approximately U-comparable, at best. Thus it is possible that the observed association between A and N is caused by some uncontrolled confounder U2, which is not a confounder of the AY association; hence, finding an unexpected association between A and N does not prove unequivocally that the A-Y association is biased. In the example of using death or hospitalization from injury as a negative control outcome for death or pneumonia/influenza hospitalization, one could argue that there may be some common causes of vaccination and injury that are not causes of all-cause death or pneumonia/influenza hospitalization. Such common causes (we cannot think of a plausible one) would create an association in the negative control analysis of vaccination and injury, even if the primary analyses of vaccination and death or pneumonia/influenza hospitalization were unconfounded— thus making the negative control detect bias even where none exists. On the other hand, if N is associated only with some, but not all, of the uncontrolled confounders of the association between A and Y, it is possible that A and N will appear unassociated despite the presence of uncontrolled confounding between A and Y. In the influenza vaccine example, one could argue that there are common causes of vaccination and death or pneumonia/influenza hospitalization = that are not causes of injury-related outcomes. Such a common cause (say, an aversion to vaccination that makes an individual less likely to get the pneumococcal vaccine) would be undetectable by this particular negative control. Despite these limitations, negative controls have value in alerting the analyst to possible residual confounding.

In principle, the measured confounders L of the A-Y relationship need not be causes of N as well, since a properly specified model that accounted for the confounding by L of A-Y would not be misled if such confounding were absent for A-N. In practice, the ideal negative control outcome should nonetheless be one with incoming arrows as similar as possible to those of Y, including the incoming arrows from L. This is true, first, because it is difficult in practice to imagine an outcome N that lacks association with known confounders L, but has an association with uncontrolled (or even unknown) confounders similar to that of U-Y. In addition, because negative controls may be useful in detecting residual confounding by measured confounders L or analytic errors, it would be beneficial to have the L-N relationship be as similar as possible, quantitatively, to the L-Y relationship. In eAppendix 1 ( http://links.lww.com ), we describe the analytic basis for use of a U-comparable negative control outcome.

A negative control exposure B should be an exposure such that the common causes of A and Y are as nearly identical as possible to the common causes of B and Y ( Fig. 3 ). To the extent that the set of unobserved common causes U of A and Y overlaps with the set of unobserved common causes of B and Y, we call the negative control exposure B “U-comparable” to A. If A and B are perfectly U-comparable and B does not cause Y, then an association B-Y when analyzed according to the same model used to analyze A-Y would indicate bias in the association A-Y. If A and B are perfectly U-comparable and B does not cause Y, then a null finding of A-N means that the A-Y association is unbiased. We are not aware of an example of the use of a negative control exposure to detect confounding in this sense. In the influenza vaccination example, one might hypothesize that whatever residual confounders U (e.g., poor health status) made one less likely to get influenza vaccine (A) and more likely to die of influenza or pneumonia (Y), might also make one less likely to get other vaccines, such as booster tetanus vaccine (B). Because tetanus does not cause pneumonia, tetanus vaccine receipt might be an appropriate negative control exposure for such a study. In the previous section, we mentioned the use of “probe variables” as negative controls to detect recall bias that might lead MS patients to over-report a history of childhood infections. Recall bias, a form of reverse causation, has a different causal structure from confounding, 1 and we do not outline here the causal requirements for negative controls to detect reverse causation.

Causal diagram showing an ideal negative control exposure B for use in evaluating studies of the causal relationship between exposure A and outcome Y. B should ideally have the same incoming arrows as A; to the extent this criterion is met, B is called U-comparable to A. Z is an instrumental variable of the A-Y relationship and is depicted to illustrate the difference between an instrumental variable and a negative control variable.

In observational settings, the comparability between exposure A and negative control exposure B will be only approximate. As in the case of negative control outcomes, this approximate comparability means that B and Y may be associated even when A-Y is unbiased; this would occur if there is some other confounder U2 linking B and Y that does not confound A-Y. Similarly, if A and B are only approximately comparable, it is possible for B and Y to show no association yet for A-Y to be biased, if the confounder biasing A-Y does not connect B to Y. An analytic basis for the use of negative control exposures is given in eAppendix 2 ( http://links.lww.com ).

In a cohort study, in which multiple exposures and outcomes are measured on each person, it is relatively straightforward to analyze negative control exposures and outcomes, assuming that suitable variables have been measured. In a case-control study, the use of negative control exposures is similarly straightforward because negative control exposures can be added to the set of exposure variables collected for each subject. If a case-control study is nested within a cohort, irrelevant outcomes can be selected and analyzed. A stand-alone case-control study presents some logistical problems for implementing negative-control outcomes. This might require a second case-control study in which “cases” include some irrelevant but comparable outcome to the cases in the main study. This difficulty is reduced if multiple control groups are used, as is occasionally done for other reasons. 11 , 12

A useful contrast can be drawn between variables that can serve as negative controls and those that can be used as instruments. 13 – 15 An instrumental variable is any variable that is connected causally to A but free of any of the confounding connections to Y from which A suffers. In contrast, a negative control outcome is connected to A through all possible confounding routes but not causally. Similarly, a negative control exposure is connected to Y through all possible confounding routes but not causally. Figure 3 depicts an instrumental variable Z that satisfies the necessary conditions of an instrument 16 , 17 while the variable B is an ideal negative exposure candidate.

We propose that negative controls should be applied more commonly in epidemiologic studies, as in laboratory experiments, and with the same goals: to detect uncontrolled confounding or other sources of bias that create a spurious causal inference. 1 The routine use of negative controls in experimental biology allows the detection of both suspected and unsuspected sources of bias. The challenge of deriving valid causal inference is at least as great in observational studies as in experiments. In other social sciences, negative control outcomes are sometimes recommended for use with observational as well as experimental studies, 18 to compensate for limited sample size and possible imbalance between treatment arms.

A.B. Hill proposed specificity of association as one guideline for assessing causal inferences. 19 Hill argued that causal inferences were more credible if the exposure (in his example, nickel mining) was associated with only certain types of outcomes (death from lung and nose cancer but not death from other cancers), and if the outcome was associated with one kind of exposure (nickel mining) but not many others. Hill himself, as well as more recent authors, 16 , 20 , 21 have been ambivalent about this particular guideline. Weiss 22 has argued that specificity of outcome and exposure may, in certain cases, lend credibility to causal inference, especially if there is a strong hypothesis of why the outcome (or exposure) should be specific to the cause. Both Hill’s and Weiss’s arguments are related to the ideas of negative controls; we suggest that informative tests of specificity of association are those that meet the criteria we have outlined for negative control exposures or outcomes. Their value will vary depending on the plausibility of the claim that the control considered is U-comparable to the exposure or outcome of interest.

Subject matter knowledge is required for the choice of negative controls, just as it is for the design of appropriate strategies to adjust for confounders. If an investigator identifies negative controls based on incorrect causal assumptions, the analysis involving negative controls may be misleading. If a causal association between two variables A-N is thought to be implausible and is used as a negative control for a study of some other association A-Y, then finding an association between A and N will erroneously suggest bias in the association AY.

A properly selected negative control is a sensitive, but blunt, tool to probe the credibility of a study. The “failure” of a negative control – the finding of an association that is judged not to be plausibly causal -- does not identify what form of bias is operating. In particular, as we demonstrate in eAppendix 3 ( http://links.lww.com ), the magnitude of bias due to uncontrolled confounding cannot generally be inferred from the magnitude of a detected A-N (or B-Y) non-null association, without extra assumptions based on firm scientific understanding. Furthermore, such additional subject matter knowledge (or suspicion about the source of analytic errors) is necessary to determine where bias is likely to have arisen.

We have defined precisely the conditions under which negative controls are capable of detecting the existence and direction of bias due to uncontrolled confounders. We have argued by example that negative controls can also aid in detecting recall bias (reverse causation) or selection bias. Epidemiologists must weigh these potential benefits of employing negative controls against the increased cost associated with the measurement of additional variables, and the possibility that the assumptions under which the negative control variables were selected are faulty.

Supplementary Material

Acknowledgments.

We thank Murray Mittleman, Molly Franke, Justin O’Hagan, and Hsien-Ho Lin for helpful discussion.

Funding: Supported by NIH 5U01GM076497 and 1U54GM088558 (Models of Infectious Disease Agent Study) to ML.

SDC Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article ( www.epidem.com ).

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Understanding Science

How science REALLY works...

Frequently asked questions about how science works

The Understanding Science site is assembling an expanded list of FAQs for the site and you can contribute. Have a question about how science works, what science is, or what it’s like to be a scientist? Send it to  [email protected] !

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What is the scientific method?

The “scientific method” is traditionally presented in the first chapter of science textbooks as a simple, linear, five- or six-step procedure for performing scientific investigations. Although the Scientific Method captures the core logic of science (testing ideas with evidence), it misrepresents many other aspects of the true process of science — the dynamic, nonlinear, and creative ways in which science is actually done. In fact, the Scientific Method more accurately describes how science is summarized  after the fact  — in textbooks and journal articles — than how scientific research is actually performed. Teachers may ask that students use the format of the scientific method to write up the results of their investigations (e.g., by reporting their  question, background information, hypothesis, study design, data analysis,  and  conclusion ), even though the process that students went through in their investigations may have involved many iterations of questioning, background research, data collection, and data analysis and even though the students’ “conclusions” will always be tentative ones. To learn more about how science really works and to see a more accurate representation of this process, visit  The  real  process of science .

Why do scientists often seem tentative about their explanations?

Scientists often seem tentative about their explanations because they are aware that those explanations could change if new evidence or perspectives come to light. When scientists write about their ideas in journal articles, they are expected to carefully analyze the evidence for and against their ideas and to be explicit about alternative explanations for what they are observing. Because they are trained to do this for their scientific writing, scientist often do the same thing when talking to the press or a broader audience about their ideas. Unfortunately, this means that they are sometimes misinterpreted as being wishy-washy or unsure of their ideas. Even worse, ideas supported by masses of evidence are sometimes discounted by the public or the press because scientists talk about those ideas in tentative terms. It’s important for the public to recognize that, while provisionality is a fundamental characteristic of scientific knowledge, scientific ideas supported by evidence are trustworthy. To learn more about provisionality in science, visit our page describing  how science builds knowledge . To learn more about how this provisionality can be misinterpreted, visit a section of the  Science toolkit .

Why is peer review useful?

Peer review helps assure the quality of published scientific work: that the authors haven’t ignored key ideas or lines of evidence, that the study was fairly-designed, that the authors were objective in their assessment of their results, etc. This means that even if you are unfamiliar with the research presented in a particular peer-reviewed study, you can trust it to meet certain standards of scientific quality. This also saves scientists time in keeping up-to-date with advances in their fields by weeding out untrustworthy studies. Peer-reviewed work isn’t necessarily correct or conclusive, but it does meet the standards of science. To learn more, visit  Scrutinizing science .

What is the difference between independent and dependent variables?

In an experiment, the independent variables are the factors that the experimenter manipulates. The dependent variable is the outcome of interest—the outcome that depends on the experimental set-up. Experiments are set-up to learn more about how the independent variable does or does not affect the dependent variable. So, for example, if you were testing a new drug to treat Alzheimer’s disease, the independent variable might be whether or not the patient received the new drug, and the dependent variable might be how well participants perform on memory tests. On the other hand, to study how the temperature, volume, and pressure of a gas are related, you might set up an experiment in which you change the volume of a gas, while keeping the temperature constant, and see how this affects the gas’s pressure. In this case, the independent variable is the gas’s volume, and the dependent variable is the pressure of the gas. The temperature of the gas is a controlled variable. To learn more about experimental design, visit Fair tests: A do-it-yourself guide .

What is a control group?

In scientific testing, a control group is a group of individuals or cases that is treated in the same way as the experimental group, but that is not exposed to the experimental treatment or factor. Results from the experimental group and control group can be compared. If the control group is treated very similarly to the experimental group, it increases our confidence that any difference in outcome is caused by the presence of the experimental treatment in the experimental group. For an example, visit our side trip  Fair tests in the field of medicine .

What is the difference between a positive and a negative control group?

A negative control group is a control group that is not exposed to the experimental treatment or to any other treatment that is expected to have an effect. A positive control group is a control group that is not exposed to the experimental treatment but that is exposed to some other treatment that is known to produce the expected effect. These sorts of controls are particularly useful for validating the experimental procedure. For example, imagine that you wanted to know if some lettuce carried bacteria. You set up an experiment in which you wipe lettuce leaves with a swab, wipe the swab on a bacterial growth plate, incubate the plate, and see what grows on the plate. As a negative control, you might just wipe a sterile swab on the growth plate. You would not expect to see any bacterial growth on this plate, and if you do, it is an indication that your swabs, plates, or incubator are contaminated with bacteria that could interfere with the results of the experiment. As a positive control, you might swab an existing colony of bacteria and wipe it on the growth plate. In this case, you  would  expect to see bacterial growth on the plate, and if you do not, it is an indication that something in your experimental set-up is preventing the growth of bacteria. Perhaps the growth plates contain an antibiotic or the incubator is set to too high a temperature. If either the positive or negative control does not produce the expected result, it indicates that the investigator should reconsider his or her experimental procedure. To learn more about experimental design, visit  Fair tests: A do-it-yourself guide .

What is a correlational study, and how is it different from an experimental study?

In a correlational study, a scientist looks for associations between variables (e.g., are people who eat lots of vegetables less likely to suffer heart attacks than others?) without manipulating any variables (e.g., without asking a group of people to eat more or fewer vegetables than they usually would). In a correlational study, researchers may be interested in any sort of statistical association — a positive relationship among variables, a negative relationship among variables, or a more complex one. Correlational studies are used in many fields (e.g., ecology, epidemiology, astronomy, etc.), but the term is frequently associated with psychology. Correlational studies are often discussed in contrast to experimental studies. In experimental studies, researchers do manipulate a variable (e.g., by asking one group of people to eat more vegetables and asking a second group of people to eat as they usually do) and investigate the effect of that change. If an experimental study is well-designed, it can tell a researcher more about the cause of an association than a correlational study of the same system can. Despite this difference, correlational studies still generate important lines of evidence for testing ideas and often serve as the inspiration for new hypotheses. Both types of study are very important in science and rely on the same logic to relate evidence to ideas. To learn more about the basic logic of scientific arguments, visit  The core of science .

What is the difference between deductive and inductive reasoning?

Deductive reasoning involves logically extrapolating from a set of premises or hypotheses. You can think of this as logical “if-then” reasoning. For example, IF an asteroid strikes Earth, and IF iridium is more prevalent in asteroids than in Earth’s crust, and IF nothing else happens to the asteroid iridium afterwards, THEN there will be a spike in iridium levels at Earth’s surface. The THEN statement is the logical consequence of the IF statements. Another case of deductive reasoning involves reasoning from a general premise or hypothesis to a specific instance. For example, based on the idea that all living things are built from cells, we might  deduce  that a jellyfish (a specific example of a living thing) has cells. Inductive reasoning, on the other hand, involves making a generalization based on many individual observations. For example, a scientist who samples rock layers from the Cretaceous-Tertiary (KT) boundary in many different places all over the world and always observes a spike in iridium may  induce  that all KT boundary layers display an iridium spike. The logical leap from many individual observations to one all-inclusive statement isn’t always warranted. For example, it’s possible that, somewhere in the world, there is a KT boundary layer without the iridium spike. Nevertheless, many individual observations often make a strong case for a more general pattern. Deductive, inductive, and other modes of reasoning are all useful in science. It’s more important to understand the logic behind these different ways of reasoning than to worry about what they are called.

What is the difference between a theory and a hypothesis?

Scientific theories are broad explanations for a wide range of phenomena, whereas hypotheses are proposed explanations for a fairly narrow set of phenomena. The difference between the two is largely one of breadth. Theories have broader explanatory power than hypotheses do and often integrate and generalize many hypotheses. To be accepted by the scientific community, both theories and hypotheses must be supported by many different lines of evidence. However, both theories and hypotheses may be modified or overturned if warranted by new evidence and perspectives.

What is a null hypothesis?

A null hypothesis is usually a statement asserting that there is no difference or no association between variables. The null hypothesis is a tool that makes it possible to use certain statistical tests to figure out if another hypothesis of interest is likely to be accurate or not. For example, if you were testing the idea that sugar makes kids hyperactive, your null hypothesis might be that there is no difference in the amount of time that kids previously given a sugary drink and kids previously given a sugar-substitute drink are able to sit still. After making your observations, you would then perform a statistical test to determine whether or not there is a significant difference between the two groups of kids in time spent sitting still.

What is Ockhams's razor?

Ockham’s razor is an idea with a long philosophical history. Today, the term is frequently used to refer to the principle of parsimony — that, when two explanations fit the observations equally well, a simpler explanation should be preferred over a more convoluted and complex explanation. Stated another way, Ockham’s razor suggests that, all else being equal, a straightforward explanation should be preferred over an explanation requiring more assumptions and sub-hypotheses. Visit  Competing ideas: Other considerations  to read more about parsimony.

What does science have to say about ghosts, ESP, and astrology?

Rigorous and well controlled scientific investigations 1  have examined these topics and have found  no  evidence supporting their usual interpretations as natural phenomena (i.e., ghosts as apparitions of the dead, ESP as the ability to read minds, and astrology as the influence of celestial bodies on human personalities and affairs) — although, of course, different people interpret these topics in different ways. Science can investigate such phenomena and explanations only if they are thought to be part of the natural world. To learn more about the differences between science and astrology, visit  Astrology: Is it scientific?  To learn more about the natural world and the sorts of questions and phenomena that science can investigate, visit  What’s  natural ?  To learn more about how science approaches the topic of ESP, visit  ESP: What can science say?

Has science had any negative effects on people or the world in general?

Knowledge generated by science has had many effects that most would classify as positive (e.g., allowing humans to treat disease or communicate instantly with people half way around the world); it also has had some effects that are often considered negative (e.g., allowing humans to build nuclear weapons or pollute the environment with industrial processes). However, it’s important to remember that the process of science and scientific knowledge are distinct from the uses to which people put that knowledge. For example, through the process of science, we have learned a lot about deadly pathogens. That knowledge might be used to develop new medications for protecting people from those pathogens (which most would consider a positive outcome), or it might be used to build biological weapons (which many would consider a negative outcome). And sometimes, the same application of scientific knowledge can have effects that would be considered both positive and negative. For example, research in the first half of the 20th century allowed chemists to create pesticides and synthetic fertilizers. Supporters argue that the spread of these technologies prevented widespread famine. However, others argue that these technologies did more harm than good to global food security. Scientific knowledge itself is neither good nor bad; however, people can choose to use that knowledge in ways that have either positive or negative effects. Furthermore, different people may make different judgments about whether the overall impact of a particular piece of scientific knowledge is positive or negative. To learn more about the applications of scientific knowledge, visit  What has science done for you lately?

1 For examples, see:

• Milton, J., and R. Wiseman. 1999. Does psi exist? Lack of replication of an anomalous process of information transfer.  Psychological Bulletin  125:387-391.
• Carlson, S. 1985. A double-blind test of astrology.  Nature  318:419-425.
• Arzy, S., M. Seeck, S. Ortigue, L. Spinelli, and O. Blanke. 2006. Induction of an illusory shadow person.  Nature  443:287.
• Gassmann, G., and D. Glindemann. 1993. Phosphane (PH 3 ) in the biosphere.  Angewandte Chemie International Edition in English  32:761-763.

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