Bias in Experiments

Bias in Experiments

Before going in-depth, let's see what bias in experiments means.

Bias can come from anywhere. It can be from the scientist conducting the experiment, the participants of the experiments or it may come from the way the experiment is being conducted. Before we go in-depth, let's learn about something called the placebo effect.

Placebo Effect and Blinding

The placebo effect is used all the time especially in the medical sector.

It involves receiving a treatment that causes improvement (or possibly side effects) even when its fake. The placebo effect is used to test the effectiveness of a treatment and if the real treatment performs much better than the placebo, then you know it really works. The participants getting the placebo should think they are getting the real thing. Otherwise, the effect may not be felt.

The participants must be blind to which type of treatment they are getting. Since the participant of the treatment should not know what type they are getting, something has to be done to make sure that it is so.

It is possible for the person administering the treatment to subconsciously give cues that may make the patient or participant know that something is wrong. For this reason, both the participant and the person administering the treatment should not know if its a placebo or not. This is called double blinding. When the patient or participant is the only one unaware of the type of treatment received, it is called single blinding.

When the placebo effect works, it doesn't mean that the illness was false. One thing that has happened is that the mind and body of the person is relaxed knowing that it is taking some kind of medication. Some symptom causing hormones may reduce as a result and the body begins to act the way it should without the illness.

That is why experiments use a control group.

For the placebo effect, one group receives the treatment while the other group receives an inactive treatment but here, one group receives the treatment but the other receives nothing at all.Let's take a look at the sources of bias in experiments.

Sources of Bias in Experiments

As earlier stated, you have bias in experiments when the experimental process is knowingly or unknowingly influenced, affecting the outcome of the experiment. Bias can come from different sources. It can come from the scientist, the participants of the experiment or the experimental environment.

Below are some sources of bias in experiments.

1. The method of data collection and the source of the data can lead to bias in experiments. To learn about the methods of data collection, see the article on Methods of Data Collection.

2. Not considering all possible outcomes can lead to bias. Even though, it is not really possible to consider all outcomes, scientist should make an effort to perform more experiments to control any new source of bias found.

3. Unknown changes in the experimental environment can lead to bias.

4. False behavior and response from the participants can lead to bias.

Let's now see some types of bias in experiments.

Types of Bias in Experiments.

The following are some types of bias.

1. Participant or Selection Bias.
2. Publication Bias.
3. Confirmation Bias
4. Observation Bias
5. Confounding Bias.
6. Design Bias.

Let's see what each of them are about.

Participant or Selection Bias

Participant bias has to do with the population. It occurs when a certain group of people are selected to participate in an experiment or research. This group of people maybe of the same age, same gender or may have the same characteristics or behavior. The problem here is that only one category of the population is considered. The experiment will not cover the effect on the rest of the population.

The way to avoid participant bias is to include various categories of people while conducting your experiment. You have to make sure that all possible beneficiaries of your experiment are investigated to know the effect on them.

Publication Bias

Publication bias occurs when only the positive or interesting aspect of a scientific study is published. There are many reasons why this happens. One reason is because people are more likely to accept your findings or product when they feel it will do little or no harm to them.

Another reason for publication bias is the standard and criteria that has been set for the publication of research papers in a certain fields. Some of these criteria may require you to leave out some information or down-play some things. The authors of these papers make adjustments so that their papers can be published.

One other reason is that those conducting the experiment may want to favor those funding the experiment thereby omitting information, especially the negative ones that may harm those funding.

Publication bias leads to limited information and understanding of a particular topic. It can also negatively affect the health and quality of living of the public.

Confirmation Bias

Confirmation bias occurs when you are carrying out an experiment for the purpose of confirming your hypothesis. The problem here is that you would want your hypothesis to be true. So, you unconsciously follow procedures and seek information that will confirm your hypothesis. You ignore everything that will say otherwise. This can lead to wrong conclusion.

You avoid this by considering all options during your experimental process and keep the possibility of your hypothesis being wrong in mind.

Observation Bias

Observation bias is seen in experiments where scientist observe the behavior of the participants. Sometimes, the participants knowingly or unknowingly act or behave in ways they would normally not behave because they know that they are being watched. Their false behavior will lead to incorrect conclusion of the experiment.

Confounding Bias

Confounding bias is a type of bias that is as a result of an external factor affecting the relationship or association between a variable or subject that is being studied and its outcome. This external factor is called a confounder. The presence of the confounder affects the accuracy of the outcome.

Design Bias

Design bias affects the outcome or conclusion of the experiment. This happens as a result of the methods and the procedures you follow while conducting the experiment. To avoid design bias, the scientist need to keep in mind all other possible bias that can occur during the experiment process and try to avoid them.

Avoiding Bias in Experiments

Avoiding bias is often called controlling for sources of bias. The following are some ways in which you can avoid bias in experiments.

1. Ensure that the participants in your experiment represent all categories that are likely to benefit from the experiment.

2. Ensure that no important findings from your experiments are left out.

3. Consider all possible outcomes while conducting your experiment.

4. Make sure your methods and procedures are clean and correct.

5. Seek the opinions of other scientists and allow them review you experiment. They maybe able to identify things you have missed.

6. Collect data from multiple sources.

7. Allow participants to review the conclusion of your experiment so they can confirm that the conclusion accurately represents what they portrayed.

8. The hypothesis of an experiment should be hidden from the participants so they don't act in favor or maybe against it.

Advantages of Eliminating Bias in Experiments

Let's see some advantages of eliminating bias in experiments.

1. The results and conclusion of the experiment will be reliable and dependable.
2. There will be better chances of the experiment helping as much people as it should.
3. Important information and findings will not be hidden or left out.
4. The conclusion of the experiment will not be influenced by any specific opinion.
5. The scientist will be open minded and consider all possibilities while conducting the experiment.
6. The data collected will be more accurate.
7. Detailed and complete articles and journals for the experiment will be published.

Examples of Bias in Science Experiments

Let's take a look at some practical examples of bias in science experiments.

Let's see another example.

Let's see another example.

Let's look at one more example.