Or, they take small samples so that chance gives them the results they want. On the other hand, liars purposely take samples that don’t accurately represent the whole to engineer the results that they want. They do this by making sure the sample is large (this reduces the effects of chance, which only has a negligible impact on large samples) and random (every entity in the group must have an equal chance of being part of the sample). To get their numbers, honest statisticians count a sample of whatever they’re studying instead of the whole (counting the whole would be too expensive and impractical) and take steps to make sure the sample’s make-up accurately represents the whole. Technique #1: Misleading With Bad Sampling You’ll also get a five-step questionnaire for evaluating the legitimacy of statistics you come across. In this summary, you’ll learn the techniques shady characters use to lie (or imply) with statistics. However, because most mistakes are usually in favor of whoever’s citing the statistic, it’s fair to assume that a lot of bad statistics are created on purpose. Some are produced by incompetent statisticians others are accidentally misreported by media who don’t understand the field. Not all bad statistics are manipulations or lies, of course. Statistics is a flexible enough field that would-be liars can make their case with implications, omissions, and distraction, rather than outright falsehoods. These people-advertisers, companies, anyone with an agenda-often don’t even have to actually lie. In How to Lie With Statistics, author Darrell Huff explains how people who want to conceal the truth manipulate numbers to come up with statistics that support their positions. However, statistics aren’t as objective as they seem. When searching for the truth, statistics are appealing-they seem like hard, believable numbers, and they’re necessary for expressing certain information, such as census data. 1-Page Summary 1-Page Book Summary of How to Lie With Statistics

How to lie with statistics huff