Recognizing the Misuse of Statistics

Instructor: Matthew Kantrowitz

Matt has a master's degree in political science and has taught at the college level

Every day we are bombarded with statistics from a variety of sources. In this lesson, you'll learn how to recognize some of the common ways statistics are misused.

Recognizing the Misuse of Statistics

  • ''My plan is clearly superior,'' the politician says. ''It saves the taxpayers 25% over the current plan, and what's more, 65% of the voters support it.''

All of these numbers sound great. They might even sound true. After all, she wouldn't just make up statistics, right? Maybe not, but there are a number of other ways to fudge the truth with stats. Numbers can be misused unintentionally, because the user doesn't know better, or intentionally, as a way to mislead people into believing things that aren't true. Let's take a look at some of the most common ways statistics are misused in everyday life.

Biased Samples

When the politician said 65% of the voters supported her plan, how could she have come up with this number? She probably gave a survey. But surveys only work if they are representative of the wider population. One way for bias to creep into statistics is by having an unrepresentative sample. What does this mean? Let's say the politician means 65% of all voters in the country support her plan. She got that number from a survey her team did of 500 voters on her email list. Do you see the problem? Five hundred people might be enough to get an accurate measure of opinion (with some error, of course), but there's no way an email list of her supporters is representative of the wider population. For one, it leaves out a big chunk of the population: her opponent's voters. By picking a favorable sample, she has skewed the result of the survey in her favor before a question has been asked.

Push Polling

In survey research, one of the most important things to remember is this: questions matter. Even if our politician took an unbiased sample, she could have asked the questions in a way to lead people to a favorable answer. Perhaps her survey question read something like, 'Do you favor plan A, which will save the lives of countless puppies, or plan B, which will condemn the puppies to a life of misery?' By painting one option in the survey as much more favorable, she has made it much more likely she'll get the response she wants. This type of polling is called push polling, as it's meant to ''push'' the respondent in a specific direction.

Ignoring Data and Errors

Let's examine the claim her plan saves the taxpayers 25%. How could she have made this claim up? One way is by ignoring unfavorable data. Perhaps, there were four studies done, two of which showed the plan saving the taxpayer 25% and two that actually showed the plan would cost the taxpayer 25% more. On average, it might be assumed that the plan won't save or cost the taxpayers anything, but by ignoring the unfavorable studies, she can claim her plan is superior. A more subtle way to cheat in this fashion is by ignoring potential errors. The survey might have been done poorly, and the margin of error might be +/-20%. That means her plan might only save the taxpayer 5%, while the current plan, assumed to be neutral, actually saves the taxpayer 20%. Of course, it could also mean her plan saves the taxpayer 45%, so it's difficult to say where the truth lies.

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