When you see an average figure, first ask: Average of what? Who's included?.The different averages come out close together when you deal with data, such as those having to do with many human characteristics, that have the grace to fall close to what is called the normal distribution.When you are told that something is an average you still don't know very much about it unless you can find out which of the common kinds of average it is: mean, median, or mode. Any questionnaire is only a sample of the possible questions and the answer given is no more than a sample of an individual’s attitudes and experiences on each question.A core problem with stratified sampling: How do you know that your information about the groups’ proportion is correct?.To get a stratified sample you divide your universe into several groups in proportion to their known prevalence.

A more economical substitute is called stratified random sampling. However, a purely random sample is difficult and expensive to obtain.The purely random sample is the only kind that can be examined with entire confidence through statistical theory.The test of a random sample: Does every individual in the population have an equal chance to be in the sample?.The dependability of a sample can be destroyed just as easily by invisible sources of bias.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.However, by the time the data from a sampling study has been filtered through layers of statistical manipulation and reduced to a decimal-pointed average, the result begins to take on an aura of conviction that a closer look at the sampling would deny.The result of a sampling study is no better than the sample it’s based on.

The problem, as with anything based on sampling, is how to read it without learning too much which is not necessarily so. 👻 Chapter 1: The sample with the built-in bias I eliminated a lot of the examples because it was difficult to summarize them without loosing the gist. The book has a lot of real world examples in government, business, adverts, etc.

This is my summary of the key ideas of How to Lie with Statistics written by Darrell Huff. How to lie with statistics - Book summary

How to lie using statistics