What is Bias in Statistics? Its Definition and Types

Being a statistics student, you should know what bias in statistics is? The majority of the students still confuse about the bias in statistics. In this blog, we are going to share with you what is bias and what are its types. Let’s get started with a short introduction to bias. Bias is all about the measurement of the process. This process helps us to get over or underestimate the value of the parameter.


Bias in statistics is a term that is used to refer to any type of error that we may find when we use the statistical analyses. We can say that it is an estimator of a parameter that may not be confusing with its degree of precision. It is the tendency of statistics, that is used to overestimate or underestimate the parameter in statistics. There are several reasons to raise bias in statistics. One of the primary reasons for this is the failure to respect either the comparability or consistency. 

Let A be a statistic used to estimate a parameter θ. If E(A)=θ +bias(θ)} then bias(θ)} is called the bias of the statistic A, where E(A) represents the expected value of the statistics A. If bias(θ)=0}, then E(A)=θ. So, A is an unbiased estimator of the true parameter, say θ.

The most important statistical bias types

Here are the most important types of bias in statistics. There are lots of bias in statistics. It is quite tough to cover all the types of bias in a single blog post.

Therefore I am going to share with you the top 8 types of bias in statistics. These biases usually affect most of your job as a data analyst and the data scientist. If you want to be one of them, then stay tuned with us. Let’s explore the top 8 types of bias in statistics. 

Selection bias 

When you are selecting the wrong set of data, then the selection bias occurs. It can be done as you are trying to get the sample from the subset of your audience apart from the entire set of the audience.

In this way, the calculation you may perform will not indicate or represent the whole population data. There are plenty of other reasons behind the selection bias, but the primary reason for this is, collecting the data from the easy to access source. Thus every time the data may obtain from the wrong source. 

For example, suppose you are doing a survey about what is the persons’ opinion regarding the presidency of Donald Trump. You have collected the data in which various individuals have well-detailed and an immediate answer to this question. But, unfortunately, various of them have a top source for their answer is Facebook Feed. It is not considered reliable as it is not public opinion but of their friends’ opinion. Therefore, this kind of data can be categorized as classic selection bias that is accessible easily but only for unrepresentative and specific subsets of the overall population.

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Self-Selection bias

Selection bias also has the subcategory, i.e., the self-selection bias. It is just like the selection. In this, you may let the analyses subject to select themselves. Suppose that in a group of people, you allow people to choose themselves based on some criteria. In the self-selection bias, there is a possibility that lazy people may not choose themselves or considered themselves as part of the group. Because it is based on a specific behavior. 

Let’s take another example of it, Suppose you are doing a survey to check the behavior of successful entrepreneurs. In this case, the answer can be uncertain as successful persons do not have enough time to answer any random survey questions. Therefore, 98% of answers will be given by entrepreneurs who believe that they are successful entrepreneurs, but actually, they are not. In this case, the surveyor (self-selector) must take a face-to-face interview with the successful persons to get accurate survey data.

Recall bias

This type of bias in statistics usually occurs in interview or survey situations, as the name suggests that it is based on the respondent’s memory power. In the interview time, when the responder doesn’t remember everything correctly, then this situation emerges the recall bias.

It is the typical scenario that we remember something, and we forget something in quick sessions. Besides, it is tough for us to remember all the things we have seen, read, listen to, or watched. It is usual for us, but when we do the survey, then it makes the survey an overwhelming process. 

For example, suppose you went on holiday 3 years ago. There is the possibility that you have forgotten the bad things, but you just remember (recall) the good things. Eventually, it does not support us to evaluate the memories, but our brains have the tendency to keep the good memories for specific reasons.

Observer bias

Observer bias is a pretty common bias. Because most of the time, the researchers subconsciously projecting or evidence his/her expectation from the research that it will be going to happen with this research. I mean to say that the researcher also tells others about their projection in many forms. For instance, influencing other participants, making some serious conversation. All these lead to observer bias. For example, it has been seen that observer bias affects the analytical research in the case of Usability tests. Being a user researcher, you can easily know about your product to which you have some expectations. If you have some experience, you might know that you should not influence your tester with questions. In case you do not have any experience in this field, remember you must have enough time to prepare unbiased and good questionnaires. You can also take the professionals’ help or idea for it.

Survivorship bias

When we need to perform the statistical operation on the pre-selection process. In this type of bias, the researcher’s evidence focuses only on the specific part of the data or study rather than the entire set of data or study. It was also missing those data-points that are not visible anymore and also fell off during this process.

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For example, there is a most interesting story collection about the statistical biases about falling cats. A study in 1987 stated that cats who fell from a higher building have some injuries compared to the cats who fell from a lower building. The phenomenon used behind it was terminal velocity. This signifies that the cat which falls from the high building reached with the maximum velocity that gave enough time to prepare for the landing. 

But after 10 years, a newspaper pointed to the fact that the cat’s chances of dying are much higher compared to the cats who fall from the lower building. The cats that fell from a higher building survived luckily. Here the survivorship bias has been included in the previous case.

Omitted Variable Bias

Sometimes we miss the most crucial element from the model of our research. In this case, the omitted variable bias occurs. This biased approach to predictive analytics. 

For example, in online businesses, business managers check the behavior of the user to make decisions for upcoming product projects. Suppose you are the manager of the company and you are observing the user activity. You make a model that estimates the user will not cancel the subscription (the estimation 75% accurate). But the next week, you found that there is a huge jump in the cancelation of the subscription. What is this? It means that strong competitors entered the market with similar services. This is what you are not prepared for. In this case, the competitor is the omitted variable for which it is impossible to prepare the predictive models.

Cause-effect Bias

Cause-effect bias is one of the most critical biases for decision-makers. But most of the decision-makers are not aware of it. It is based on the simple formula that correlation does not imply causation.

For example, students who had professors in the high schools scored bad grades compared to the students who did not have. This might look like a misguided picture or might not link to a real example. But here the point is that here the tutoring method was not the cause of scoring the bad grades, but the bad score was the cause of effects. This specifies that there is a need for a good education method.

Funding Bias

The funding bias is also known as sponsorship bias. When the scientific suggestions or study results are biased in favor of financial sponsors of the research, then funding bias occurs. 

For example, the researcher of Duke University did a study related to A Sugar Association funds found that there are various adverse effects on health by consuming the Splenda (Sucralose). Here is a point that the study’s sponsor was interested in alerting society about artificial sweeteners. But other organizations, such as the FDA, WHO, and others (who mostly sponsored their research), indicated that sucralose components are safe to use.

A quiz: How do you identify bias in a sample

Case 1: Zen hosts a program, and he is very excited to know how his audience likes the show. Therefore, he decides to do an online survey in which he asks the listeners to go to the website and participate in the surveys. 

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The survey shows that 89% of the audience love the show.

1. What is the respecting source of bias in this particular survey?

(A) Response bias

(B) Voluntary response sampling

(C) Undercoverage

2. Which direction of bias is applied in this case?

(A) 89% is an overestimated percentage of all audiences who love the program.

(B) 89% is an underestimated percentage of all audiences who love the program.

(C) 89% is an unbiased estimation.

Case 2: Zen hosts a program, and he is very excited to know how his audience likes the show. He decided that he will do some surveys of 100 audiences who send the fan email to him. 

Not all the audience persons respond, but 94 out of 97 audiences respond as they “love” the show.

1. What is the respecting source of bias in this particular survey?

(A) Nonresponse

(B) Voluntary response sampling

(C) Convenience sampling

2. Which direction of bias is applied in this case?

(A) The result is an underestimated percentage of all audiences who love the program.

(B) The result is an unbiased estimation.

(C) The result is an overestimated percentage of all audiences who love the program.

Case 3: A high school decided to survey what percentage of the students smoke cigarettes. When students went to the counselors to know about scheduled classes, he/she asked each student whether they smoked cigarettes or not.

The survey’s result is that 5% of students do smoking.

1. What is the respecting source of bias in this particular survey?

(A) Response bias

(B) Voluntary response

(C) Biased wording

2Which direction of bias is applied in this case?

(A) The result is an unbiased estimation.

(B) The result is an underestimated percentage of all audiences who love the program.

(C) The result is an overestimated percentage of all audiences who love the program.

Correct Answers:

(B)It is a voluntary response sample that mostly produces biased outputs.
(A)People who enjoy the show visit the website and answer the survey questions without any discussion with others.
(C)Here, the result got from the particular audience, not from the randomly selected audiences.
(C)Audiences who send a fan email to Zen show have loved the program in comparison to a typical audience.
(A)Response bias cause if people are regularly dishonest about the answer to a question.
(B)There is the possibility that students who answered that they smoke, but actually they don’t or vice-versa. Therefore, it is underestimated result.


There are a lot more types of bias in statistics. But we have covered the most crucial one. Now it might be clear in your mind that what is bias and how it occurs in statistics.

If you need any help regrading the bias in statistics then you can get into touch with our experts. They will solve all your queries as soon as possible. Also get the best excel homework help from the experts at nominal charges.

Frequently Asked Questions

What is bias in statistics?

Any type of error in statistics that we found with the use of statistical analyses is known as bias in statistics. In other words when we want to refers any error in statistics, we call it the bias.

What are the most important statistical bias types

Selection bias 
Self-Selection bias
Survivorship bias
Recall bias
Cause-effect Bias

What causes bias in statistics?

Most bias is caused by sampling bias. Suppose some differences are caused not only due to chances but also caused by sampling bias. Sampling bias occurs when certain variable values might systematically lack or over-represented or under-represented regarding the true variable distributions.


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