We've all experienced some form of bias in one way or the other. You may have seen it happen to others, experienced it yourself or even participated in it. Bias here means favoring something over another even when the thing being favored does not deserve to be.
Aside from our everyday lives, bias also occurs during experiments and research. In this article, you will learn about the sources, types and examples of bias in experiments.
Bias in Experiments
Before going in-depth, let's see what bias in experiments means.
Bias in experiments refers to a known or unknown influence in the experimental process, data or results.
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.
A placebo is a medicine or procedure that has no active substance and no real effect.
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.
Blinding means to keep information from someone about the type of treatment they are getting.
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.
A control group is a group that does not receive any treatment during an experiment.
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.
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.
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.
Unknown changes in the experimental environment can lead to bias.
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.
- Participant or Selection Bias.
- Publication Bias.
- Confirmation Bias
- Observation Bias
- Confounding Bias.
- 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.
For example, if you have a new vaccine that you want to test and you test it only on healthy people who are between the ages of 20 to 30 years old. The data you will get from this test cannot tell how effective the vaccine will be on the entire population. Your test does not give you information on the effect on people younger than 20, people older than 30 or people with underlying health conditions. The data from this experiment is not sufficient to release this vaccine to the public.
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.
This bias is seen a lot in the medical sector when a new drug or treatment method is being introduced. Sometimes, they want to down-play the negative effect of what they are proposing so it can be accepted. That is why in the US when you see an ad for a new drug it has to list all of the possible side effects for the drug.
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.
Ensure that the participants in your experiment represent all categories that are likely to benefit from the experiment.
Ensure that no important findings from your experiments are left out.
Consider all possible outcomes while conducting your experiment.
Make sure your methods and procedures are clean and correct.
Seek the opinions of other scientists and allow them review you experiment. They maybe able to identify things you have missed.
Collect data from multiple sources.
Allow participants to review the conclusion of your experiment so they can confirm that the conclusion accurately represents what they portrayed.
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.
- The results and conclusion of the experiment will be reliable and dependable.
- There will be better chances of the experiment helping as much people as it should.
- Important information and findings will not be hidden or left out.
- The conclusion of the experiment will not be influenced by any specific opinion.
- The scientist will be open minded and consider all possibilities while conducting the experiment.
- The data collected will be more accurate.
- 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.
You have an hypothesis that artificial coloring of food causes hyperactivity in children. To investigate this, you take two groups of children and give one group fruits and the other group artificial colored sweets. The group of children that ate the artificial colored sweets were hyperactive which confirms the hypothesis.
What kind of bias can you identify in this experiment and explain why it is a bias?
Solution:
The type of bias here is confirmation bias. You have not considered that those group of children were hyperactive because of the sugar they were consuming, or the fact that they haven't been getting much exercise, and not because of the artificial coloring.
Let's see another example.
You are conducting an experiment to see the effect of a particular supplement in young males. Over 60% of the participants are African Americans and the rest are Caucasians.
What kind of bias can you identify for this experiment and explain why it is a bias?
Solution:
The bias here is participant or selection bias. With your participants, there is under representation and over representation of two groups of people and you have not even considered other races. Unless your research is exclusive to a particular race, your participants have to be diverse.
Let's see another example.
For the purpose of meeting some publication criteria or guidelines, you have decided to omit some useful information from your research.
What type of bias is this?
Solution:
This type of bias is called publication bias.
Let's look at one more example.
You are trying to study the behavior of a group of people. The participants of the experiment are aware of the experiment hypothesis and they also know they are being watched. Because of this, they try to act in ways that they feel is acceptable.
What kind of bias can you identify here?
Solution:
This type of bias is called observation bias. The hypothesis of an experiment should be hidden from the participants so they don't act in favor or against it.
Bias in Experiments - Key takeaways
- Bias in experiments refers to a known or unknown influence in the experimental process, data or results.
- Some types of bias are below.
- Participant or Selection Bias.
- Publication Bias.
- Confirmation Bias
- Observation Bias
- Confounding Bias.
- Design Bias.
- 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.
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