Bias in statistics- A Statistics Student Should Know

If you are a statistics student you should know the various biases in the statistical processes of measuring.

Bias terms in statistics denote the difference between the real value and expected value of the parameter. Bias is a systematic variation or fluctuation from the real value. This sampling procedure creates serious problems for the researchers when they calculate the sample size.

Do you want to know the bias in statistics in detail?

If yes, your search ends here. In this blog, we will explore various types of bias and classification. Let’s start with the definition of bias in statistics.

Bias in Statistics

Bias is a statistical term that represents the disposition of the process to measure. It refers to the determination value of a population criterion or parameter with either over or underestimation. Let’s understand it with the help of an example.

Suppose you know the rule to calculate the median of the population and get a prediction with the help of that rule. Now, the value you get may be a true reflection of the population hence; it will be easy for you to evaluate the difference between the true value and expected value of a parameter by using the biased estimator.

What are the types of Bias in statistics?

In statistics calculations, various biases occur in the calculation process. Below are some types of Bias that we face in the measuring process.

  1. Selection Bias
  2. Cognitive Bias
  3. Spectrum Bias
  4. Exclusive Bias
  5. Analytical Bias
  6. Reporting Bias
  7. Data Snooping Bias
  8. Omitted Variable Bias
  9. Funding Bias

How do we classify the Bias in statistics?

Primarily Bias is categorized into two types: measurement bias and non-representative sampling bias.

Measurement Bias

Measurement bias is also known as observation or information bias. It occurs when there is a wrong collection, interpretation, and measurement of the information. Johns Hopkins defines it as the information, when collected not similarly, creates an error in the conclusion.

Suppose you want to know about a perfume brand that you may not have consumed. If the arrangement of questions in a questionnaire is not proper, you will get confused, and people may not respond properly, and they can mistakenly answer as not consumed even if the same person has consumed it.

 Below are some causes of information bias.

Error while the collection of Data- When the researcher does not collect or handle the data appropriately and when due to the malfunction of machines or tools causes the data collection error. A researcher who deals with data collection should know how to handle data and use tools correctly.

Poor arrangement of Questionnaire: If the interviewer does not arrange the questions properly, the error is introduced due to the faulty questionnaire. The interviewer arranges the questions for the survey to lead to the answers that the researcher prefers. More choices may be there in comparison to conflicting views.

False Responses of the respondents: A measurement error occurs due to the false or not correct answers that the respondents give to the interviewer. 

Especially in the case of elderly people, when the surveyor asks about their experiences and wants to have an answer according to their experiences, they may not be able to give a proper answer. The reason behind this is they do not have accurate records of their gone days.

Non-Representative Bias

When a survey sample cannot demonstrate the population’s features or characteristics appropriately, non-representative Bias occurs. This happens due to working on a particular group of the population rather than the entire population. 

In this case, the sample results are unrepresentative of the entire population. It is also known as selection bias.

Here are some main types of selection(non-representative) Bias in statistics.

 Undercoverage Bias: The reason for occurring under coverage Bias is the absence of some respondents of the population from the sample. The primary reason behind this Bias is convenience sampling, which means collecting data from an easily available source such as a supermarket.

Non-response Bias: In case of non-response bias, identifying individuals who do not intend to attend the survey is made. So, the respondents have the power over the survey result, whereas the contradictory views of non-respondent remain unnoticed.

Voluntary Response Bias: As the name suggests, this Bias occurs when a sample participant is a self-selected volunteer. For example, call-in radio shows and other similar types of responses from the voluntary callers provide the inappropriate expression of the overall population.

Volunteer Bias: It is a situation or circumstances when the population that volunteers for the trials can not demonstrate the target respondents.

Survivorship Bias: The reason behind a survivorship bias is biased sampling due to the calls for the survival of a lengthy process. In order to read it as a complete response leads to biased sampling.

Confirmation Bias- when the information on samples is related to one belief, it causes confirmation bias.


In this blog we have discussed the various bias in statistics. We do different calculations and measurements in the statistics field, we meet with the various types of eros or bias. There are various factors that are responsible for such biases and we have explored them in this blog. I hope now you are aware of the above types of bias and reasons why they happen.


Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Back to top button