More: ““27 percent of a large sample of eminent physicians smoke Throaties- more than any other brand.” The figure itself may be phony, of course, in any of several ways, but that really doesn’t make any difference. “If you can’t prove what you want to prove, demonstrate something else and pretend that they are the same thing.”įor example: “You can’t prove that your nostrum cures colds, but you can publish (in large type) a sworn laboratory report that half an ounce of the stuff killed 31,108 germs in a test tube in eleven seconds.”

You have to look at the range of data being used on both axes. These three are all the same graph (data wise), but very different impression wise: With 10 coin flips you might get 8 heads, but you’re much less likely to get 80 heads in 100 coin flips. With smaller samples you have larger variance.

Discarded DataĬompanies will keep running experiments until they get the results they want, discarding the experiments that “failed to produce significant findings.” annual household income) you’ll get vastly different numbers for each. In normal distributions, the three will be near each other, but in irregular distributions (e.g. These can be very different numbers, and reporters and others will pick the one that best supports their argument. The mean: add up all the values and divide by the quantity of valuesThe mode: the most common valueThe median: the value in the middle of the sample If a psychiatrist says that “practically everyone is neurotic,” do you suppose that their impression has been biased by their line of work? Biased Averages “To be worth much, a report based on sampling must use a representative sample, which is one from which every source of bias has been removed.” When you hear a statistic, say, that the average American brushes their teeth 1.02 times a day, ask yourself: “How could they have figured it out?” Does it make sense that it could have been researched effectively? In this case, they would have had to ask, and don’t you think it’s a safe assumption people lied? Many conclusions you see come from samples that are too small, biased, or both. Don’t be fooled by missing data: Just because taking a drug clears up a cold in one week doesn’t mean that it wouldn’t have cleared up in a week on its own.

Book how to lie with statistics