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How to Identify Statistical Bias

Bias is a word you hear all the time in statistics, and you probably know that it means something bad. But what really constitutes bias? Bias is systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results. Bias can occur in any of a number of ways:

  • In the way the sample is selected. For example, if you want to estimate how much holiday shopping people in the United States plan to do this year, and you take your clipboard and head out to a shopping mall on the day after Thanksgiving to ask customers about their shopping plans, you have bias in your sampling process. Your sample tends to favor those die-hard shoppers at that particular mall who were braving the massive crowds on that day known to retailers and shoppers as “Black Friday.”

  • In the way data are collected. Poll questions are a major source of bias. Because researchers are often looking for a particular result, the questions they ask can often reflect and lead to that expected result. For example, the issue of a tax levy to help support local schools is something every voter faces at one time or another. A poll question asking, “Don’t you think it would be a great investment in our future to support the local schools?” has a bit of bias. On the other hand, so does “Aren’t you tired of paying money out of your pocket to educate other people’s children?” Question wording can have a huge impact on results.

Other issues that result in bias with polls are timing, length, level of question difficulty, and the manner in which the individuals in the sample were contacted (phone, mail, door-to-door, and so on).

When examining polling results that are important to you or that you’re particularly interested in, find out what questions were asked and exactly how the questions were worded before drawing your conclusions about the results.

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