Bias in Polls & Surveys: Definition, Common Sources & Examples

Bias in Polls & Surveys: Definition, Common Sources & Examples
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  • 0:01 What Is Bias?
  • 1:13 Why Be Biased?
  • 1:56 Sources of Bias
  • 3:45 Lesson Summary
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Lesson Transcript
Instructor: Kevin Newton

Kevin has edited encyclopedias, taught middle and high school history, and has a master's degree in Islamic law.

When Mark Twain commented that there were three types of lies, he included statistics in the count. In this lesson, we look at bias, one of the ways in which statistics can mislead, and in some cases, flat out lie to us.

What Is Bias?

Let's say you were sitting at home cleaning up dinner. Your phone rings, and while the number is unknown, you are eager for an excuse to stick your little brother with the dishes. You say hello and a voice asks you if you'd be willing to answer some questions on the performance of your city mayor. Since your favorite morning routine is reading the local news, you feel pretty well-informed to give your opinions on her performance. The caller asks you which of the following words best sum up your opinion of the mayor: fair, poor, bad, or horrible?

Wait a minute, you wonder, maybe you missed a choice. You actually think that your mayor has been doing an exceptionally great job. You ask the pollster about this and are told that those four--fair, poor, bad, and horrible--are your only options. You shrug your shoulders and say fair, knowing that it's really not how you feel. As you hang up, you can't help but wonder if you're the only one.

The poll you just took part in was full of bias. Bias can come in many shapes, as we will see in this lesson, but always has the effect of creating a data set that is markedly different from what the reality actually is. While bias can be dangerous in pharmaceutical experiments, it can be used by pollsters to further their own means.

Why Be Biased?

Chances are that the call you just took was funded by someone who didn't like the mayor. We can tell this because the answers are structured in such a way that more people could say that the mayor is doing a bad job. Statistics make great news stories, so this increases the chance of enough people saying that the mayor is doing a bad job so that the newspaper would publish a story on the findings of the poll.

As such, bias can be used to make the other side look bad. It can also be used to make one's own side look great. Imagine the same question, but this time with answer choices like superior, great, good, and fair. Now someone who legitimately doesn't like the work that the mayor has been doing is forced to say fair, when that's not really how they feel.

Sources of Bias

Still, whether or not a party intends on being biased, there are plenty of sources possible. The kind of bias that you suspect from above, where someone who didn't like the mayor paid for the poll, is called funding bias. As you'd imagine, this is the kind of bias that results when the study is done by a company or individual that has an interest in the final outcome. Think about how big tobacco companies used to use statistics to lessen the connection between smoking and lung cancer, and you've got a good idea of what funding bias looks like.

The type of bias that involves the choice of answers is reporting bias, since only answers of a certain variety are likely to be reported. In the phone call you got, there was no way to record an answer of superior performance by the mayor, so those sentiments were not expressed in the final data set. For a second, let's even think about why you were called.

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