# Bias in Statistics: Definition & Examples

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• 0:01 Faults in Statistics
• 0:26 Bias in Statistics
• 0:57 Selection Bias
• 3:02 Response Bias
• 5:40 Lesson Summary

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Lesson Transcript
Instructor: Cathryn Jackson

Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

Statistics can be a powerful tool in research. Unfortunately, statistics can also have faults. In this lesson, you will learn about the faults in statistics and how to critically examine potential biases in research.

## Faults in Statistics

Sam is working on an experiment for his health education class. He wants to explore how a healthy diet can impact a student's academic performance. However, he runs into several issues when conducting his experiment and analyzing his data. Sam is afraid there is bias in his research, but he doesn't know where to look for biases.

In this lesson, you will learn about selection and response biases. First, we will discuss response biases.

## Bias in Statistics

Sam has conducted a survey to get more information about healthy diets. While there is nothing wrong with survey research and the information that Sam wants to know, there are some potential biases that can happen. A bias is the intentional or unintentional favoring of one group or outcome over other potential groups or outcomes in the population.

These biases can come in the form of two main categories: selection bias and response bias.

## Selection Bias

First, let's talk about the selection biases that can occur, including non-representative sample, nonresponse bias, and voluntary bias.

A non-representative sample refers to when the method with which a sample is selected specifically excludes certain groups from the research, whether intentionally or unintentionally. For example, let's say that Sam asks only the students in the health and physical education department at his college to participate in his survey. If Sam only uses students from the health and physical education department, then he is not representing the entire college population, only those from that department. Sometimes, it is difficult to conduct experiments that are completely representative of the population. However, it is important to be aware of these factors and the groups that could be left out of your experiments.

A nonresponse bias describes the members of a sample that do not choose to respond or participate in the research and the characteristics of those members. For example, let's say that Sam hands his survey out to 100 people in the cafeteria at his college. But only 45 people choose to participate in the survey, leaving 55 people that did not respond. The people that choose not to respond to the survey have certain characteristics that will prevent Sam from inferring parameters about the whole population and creating a representative sample. Unfortunately, this is something that the researcher has no control over.

A voluntary bias describes the members of a sample that choose to respond or participate in the research, whether intentionally or unintentionally. For example, remember that only 45 people responded to the survey that Sam handed out. These individuals may have some similar characteristics, which would also make this group a biased and non-representative group for his research.

## Response Bias

Next, let's talk about certain response biases that can occur. This can happen through design faults, such as constructing a survey with leading questions, or it can happen through a fault of the respondent, such as a tendency known as social desirability.

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