Experimenter Bias is a type of cognitive bias that occurs when experimenters allow their expectations to affect their interpretation of observations. People believe that bias is rare, but its presence can seriously threaten the validity of an experiment. 

In this article, we will consider the concept of experimental bias and how it can be identified.

What is Experimenter Bias

Experimenter Bias is a type of cognitive bias, which means there is a systematic pattern of deviation from an objective performance or analysis. Such deviations are caused by internal and external factors, such as the circumstances, beliefs, how the experimenter was conditioned in his or her earlier years.

The concept of experimenter bias has been around for over 50 years. A study conducted by Psychologists Peter Wason and Susan Young published in 1960 illustrated the concept of experimenter bias. It defines an experiment as a test performed to prove or disprove any hypothesis, thought, or theory.

Read: What is Participant Bias? How to Detect & Avoid It

Therefore, experimenter bias refers to all human errors associated with the research process. It occurs because of the behavior of the scientist or researcher conducting the experiment.

Experimental bias can affect any sort of observation, as an experimenter might expect certain results based on previous knowledge, even if such expectation is not part of the hypothesis being tested. However, it is important to note that experimental design must eliminate bias if it is going to be scientifically valid. 

If the researcher is observing an experiment and believes something will happen (without actually influencing it), then this does not constitute experimenter bias because the individual’s preconceived expectations have no influence on the outcome of the study.

Read: What is Publication Bias? (How to Detect & Avoid It)

How Do You Identify Experimenter Bias?

There are a few ways to identify experimenter bias in a study.

1. Look at the language used in the experiment’s description. If the writer is presenting the experiment as if it’s already successful, they might be trying to influence the results. For example, a researcher may describe how they expected their study to turn out and then discuss their results in terms of whether they matched these expectations.
2. Compare the size of the sample used in the experiment with how long it takes to collect results. If there’s no way they could have collected enough data for statistical significance during that time, they might be making up their results or only showing you data that supports their hypothesis.
3. Check for any correlations between what someone else does and what happens. If there’s a pattern to which experimental groups succeed and which ones fail, that might indicate that someone is intentionally manipulating their results.

What are the Types of Experimenter Bias?

There are three main types of experimenter bias that can lead to both inaccurate data and poor evaluation of results: self-fulfilling prophecy, observer bias, and interpreter bias.

1. The self-fulfilling bias: The self-fulfilling bias effect occurs when a researcher’s expectations about how a study will turn out, influence what they observe. When conducting research, it’s important to be as objective as possible in order to prevent these kinds of biases.
2. The subject bias: The subject bias effect occurs when a subject’s expectations about how a study will turn out influence their behavior. For example, someone may think that a drug will make them feel sick and then experience nausea as a result. In order to prevent this type of experimenter bias from affecting results, researchers should take steps when designing their studies to account for potential impacts on subjects’ behavior.
3. Observer bias: The observer bias effect occurs when an observer’s expectations about an experiment affect how they interpret results. A good way to minimize this type of bias is by having multiple observers evaluate test results rather than relying on just one observer to avoid confirmation bias or other subjective interpretations that may occur.

Read: Sampling Bias: Definition, Types + [Examples]

Effects and Implications of Experimenter Bias

The effects of experimenter bias can be detrimental to that experiment’s results, and potentially affect other research that relies on those findings. The most well-known example of this is the Stanley Milgram experiments from the 1960s, a series of tests where participants, told they were partaking in a study on learning and memory, were asked to electrically shock a learner each time they got an answer wrong in a memory test. 

The shocks started small but increased with every mistake until they became lethal. Despite the fact that the learner was not actually being shocked (and instead was acting), two-thirds of subjects continued administering shocks all the way up to lethal levels when told by researchers to do so. While the Milgram tests may be extreme (and ethically controversial), it’s important to recognize that experimenter bias can occur in all experiments.

In other words, if you’re running an experiment to test whether people prefer a water bottle that is blue or red and you expect that blue is going to win, you might accidentally create conditions during your experiment that lead to more positive experiences for those in the blue group. Experimenter bias can affect what the researcher chooses to measure and how they interpret their results, and it can lead to incorrect conclusions.

Read: Undercoverage Bias: Definition, Examples in Survey Research

Experimenter bias also shows up in situations where researchers choose not to publish studies with unexpected results. This process can lead to a biased set of published literature, which then influences future research and causes even more biases to be introduced into the field.

Examples of Experimenter Bias

Example 1

Let’s say that you are an experimenter and you believe that eating cherries will cure headaches. You decide to test this by asking people with headaches to eat cherries and then reporting whether their headache has gone away. So, almost all of them report feeling better after eating cherries.

It seems like you have stumbled upon a miracle fruit! However, before you run out to buy a truckload of cherries, take a look at your study. There is a high probability that your experiment is suffering from experimenter bias. 

This is because you had reason to believe that cherries would cure headaches, so it is likely that you inadvertently influenced the results of your study: perhaps by asking leading questions or unintentionally influencing how participants interpreted your survey. As a result, those who were not given cherries may have reported feeling better simply because they knew that you were expecting them to feel better after eating cherries.

Example 2

If a researcher believes that a person should be able to read aloud faster when they’re standing on one leg, and that’s what happened in the study, it could be because the researcher-led participants through the study in such a way that they were more likely to perform better under those conditions. The researcher’s expectation (that people read better while standing on one leg) influenced how they interpreted their results.

How to Avoid Experimenter Bias

There are several ways that experimenter bias can be prevented: 

1. Use multiple observers during the study and compare their observations at the end to see if there were any differences in how they conducted their study.
2. Use random assignment of subjects into groups so that one group doesn’t know what they’re supposed to do while another group does know what they’re supposed to do (this will help reduce experimenter expectation effects).
3. Use double-blind experiments where neither the experimenters nor participants know which treatment has been administered until after all data has been collected and analyzed.”

Experimenter Bias vs Investigator Effects

Experimenter bias is the tendency of a scientist or researcher to introduce bias into an experiment. The bias can come in a variety of forms including manipulating results, choosing certain participants knowingly, and choosing certain participants unknowingly.

If a researcher knows the outcome of their experiment beforehand, it is possible that they may introduce bias into their research in order to prove their hypothesis correct. For example, if a scientist is studying the effects of smoking and already knows that smoking has negative effects on the body, they may perform an experiment where they force their participants to smoke excessive amounts of cigarettes in order to prove their hypothesis correct.

Investigator effects are similar but are often discussed as a different topic. When discussing investigator effects, most researchers refer to biases introduced by the experimenter’s behavior during the study. This could include things like attitude, facial expressions, body language, or anything else that could potentially alter the participant’s response.

Both experimenter biases and investigator effects can be controlled by using double-blind experiments which ensures that neither the researcher nor participant knows who is receiving what treatment or intervention.

Read – Internal Validity in Research: Definition, Threats, Examples

Experimenter Bias vs Confirmation Bias

Experimenter bias is a form of bias that’s also known as expectancy bias, and it’s a common problem that can skew the results of an experiment. It happens when the people conducting the experiments have a certain expectation about what will happen to the subjects and are unconsciously influenced by their preconceived notions.

Confirmation bias, on the other hand, happens during the analysis of data, where those analyzing it look for information that confirms their earlier expectations or beliefs. In both cases, these biases can be detrimental to producing accurate results.

Conclusion

Experimenter bias can affect the result of research but it can be avoided by making sure that researchers are being thorough in checking all of their data, and by having multiple people look over the research before publishing it to catch any potential biases.