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How To Lie With Statistics Summary

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Sandra Watson
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How To Lie With Statistics Summary – How to Read a Paper

How To Lie With Statistics Summary
1. The Sample with the Built-in Bias
Response Bias: Tendency for people to over- or under-state the truth
Non-response: People who complete surveys are systematically different from those who fail to
respond. Accessibility/Pride.
Representative Sample: One where all sources of bias have been removed. (Literary Digest)
Questionnaire wording/Interviewer effects
Recall Bias: Tendency for one group to remember prior exposure in retrospective studies
The sample with a built-in bias : the origin of the statistics problems – the sample. Any statistic is
based on some sample (because the whole population can’t be tested) and every sample has some
sort of bias, even if the person wanting the statistic tries hard to not create any. The built-in bias
comes from the respondents not replying honestly, the market researcher picking a sample that gives
better numbers, personal biases based on the respondent’s perception of the market researcher, data
not being available at a certain past time are a few of the biases that creep in when building a
statistic. One of the example (from the 1950s) that the author mentions is a readership survey of two
magazines. Respondents were asked which magazine they read the most – Harpers or True love story.
Most respondents came back that they read the True Love Story, but that publisher’s figures came
back that the True Love Story had a much higher circulation than Harpers – refuting the results from
the sampling.

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How To Lie With Statistics Summary

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