# Confounding & Bias in Statistics: Definition & Examples

Lesson Transcript
Instructor: Kevin Newton

Kevin has edited encyclopedias, taught history, and has an MA in Islamic law/finance. He has since founded his own financial advice firm, Newton Analytical.

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 research. Updated: 10/15/2019

## Problems with Statistical Analysis

Statistics are pretty useful tools when placed in the proper hands. They can help politicians figure out how to best plan their campaigns, alert educators to students with exceptional needs, assist researchers in creating new medications, or provide businesses with some idea of how their customers act. However, in unskilled hands, statistics can be more trouble than they're actually worth. Even when intent is honest, statistical analysis can often prove things that are far from true. In this lesson, we're going to look at two particular problems that affect statistics, as well as looking at examples of their incidence.

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## What Is Confounding?

When planning a statistical analysis, whether it is a simple poll or a double-blind study, researchers should be wary of introducing any sort of problem into the sample. After all, figuring out what a set of data predicts is often hard enough without having to sort through added mistakes! Any sort of problem that lessens the likelihood of statistical data to be able to present a firm and realistic set of answers is called confounding. Confounding can happen when a data set is corrupted through poor gathering techniques, as well as when the whole experiment itself is set up without enough controls. Controls are the methods used by statisticians to make sure that only the issue at hand is being examined. For example, if you were going to ask a group of affluent people where they went to the grocery store, some controls would include only asking wealthy people or only asking people who live in an area with many grocery stores. Likewise, if you were preparing a double blind study to see if one soft drink was more popular than another, you would control for race, gender, and economic background.

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