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There are too many ways to list, really, but here are a few common ones.

First, and probably most common, is to assume that a statistical relationship equals a cause and effect one. You can, for example, quite accurately predict the damage a fire will do by counting the number of firefighters who show up to put it out. But that does not mean that firefighters cause fire damage. Other examples of this abuse can be seen everywhere in advertising. Just because kids who eat a healthy breakfast do better in school, that does not mean that the breakfast caused it or that if you suddenly start eating better your grades will improve. More likely, parents who have the sense and caring to prepare a healthy breakfast caused the kids to do better in school.

Second, you can ignore other contributing variables. The classic example here is the fact that predominantly non-white neighborhoods have higher crime rates. For years, this statistic was touted as proof that non-whites are inherently violent and criminal-minded. Yet when you also consider the economics of a neighborhood, it turns out that poverty leads to higher crime, not skin color.

Lastly, and this one is thankfully rare but also the most devious, you can intentionally delete cases or otherwise manipulate data to achieve the results you want. (Despite claims to the contrary, this rarely happens in legitimate science).

But in general, a misuse of statistics has occurred any time that you rely too heavily on the numbers and forget that they are just numbers. If there is no practical connection between the numbers and what they represent, no common sense analysis of what the numbers mean and what could have been missed, then statistics is nothing more than just fancy math and fodder for sound bites on the evening news.

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โˆ™ 2009-12-24 07:27:28
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Q: How statistics can be misused?
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