How to Lie with Statistics – Part 3: The Little Figures That Are Not There

Continuing with my previous post, here I summarize Chapter 3 of the book How to Lie with Statistics. This chapter is titled, The Little Figures That Are Not There.

The key thought of this chapter is about what is left unsaid when a particular statistic is illustrated – an average without a range, for example. Today’s post about COVID metric reporting was also illustrative of this point. Take an “average” temperature of any given city and that tells you nothing if a range is excluded. Or better yet, when no information is given as relates to sample size or method of deriving the statistic.

Many times a sample statistic may be passed as representative of the whole, but in fact the sample being singled out is merely one of many taken and conveniently left out (if it doesn’t fit the agenda being pushed, of course).  In addition, “how likely it is that a test figure represents a real result rather than something produce by chance”? In these instances what you should be told is some sort of degree of probability telling you something about the statistical significance of the results. In plain English, how likely are the results truly indicative of the population as a whole? In no uncertain terms you should be told what that likelihood is.

Furthermore, be skeptical of charts that do not have proper scales or at worst deliberately exclude information.