Sometimes a researcher might (again, unwittingly) present a statistically significant finding as a big deal, even if it is, in fact, simply spurious and the result of the researcher conducting too many analytical tests. If you conduct 1,000 significance tests, you are likely to find a few of them as significant simply by chance alone. How To Lie With Statistics by Darrell Huff Sampling Errors Biased Sampling The different types of averages How you can use and unrelated number to answer. That simply means a result erroneously comes out as statistically significant. They allow us to describe and find out about the way we live in every sense. Simple Ways to Lie Missing Figures Knowing just an average can be worse than knowing nothing Example American housing: Mean of 3. And, statistics are a valid and useful tool. In short, the more significance tests someone conducts during the analytical stage, the more likely he or she is to find a spurious result. Yes, using statistics to lie is easy - as you will soon see. It often results from a scenario in which a researcher conducted a plethora of significance tests as part of the analytical process. Darrell Huffs How to Lie with Statistics remains the best-known, nontechnical call for critical thinking about statistics. The math we learn in school can seem like a dull set of rules, laid down. This little book is a great piece to get one in the right mindset for consuming media, political propaganda, science, and more While it is a lighthearted read, lacking in substantial statistical analysis, it does provide a basic breakdown of how statistics can be used to manipulate, fool, and lie (with oneself as a thinker, and with ones consumers).

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This kind of outcome is actually more common than one might think. How Not to Be Wrong: The Power of Mathematical Thinking. Without getting too bogged down in the details, Type-I Error exists when a researcher finds that one result is statistically significant but, in fact, the reality is that the findings do NOT generalize to the broader population of interest.

How to lie with statistics