Name: Sex by Numbers
ISBN: 9781781253298

Sex by Numbers

Whatever society we live in, and however open-minded we like to think we are, when it comes to our sex lives we all like to keep a few secrets. But this makes the jobs of sexologists - professionals who study sexual behaviour - pretty difficult. Luckily, David Spiegelhalter, Professor of Risk at Cambridge University, is here to unravel the web of exaggerations, misdirections and downright lies that surround sex in modern society. Drawing on the NATSAL survey, the widest survey of sexual behaviour since the Kinsey Report, he answers crucial questions such as what are we all doing? How often? And how has it changed? Accompanying a major Wellcome exhibition on the same subject, Sex by Numbers is an informed and entertaining look at the most enduring of human obsessions, from one-night stands to transexuality.

What Hacker News says

staredJan 2019

For practical statistic done well, I recommend "Sex by Numbers" by David Spiegelhalter (https://www.amazon.com/Sex-Numbers-Wellcome-David-Spiegelhal...).

I am reading Sex by Numbers, which I enjoy a lot. It's a touchy subject with a lot of data of varying quality. A naive approach would be to take all of it. A dogmatic - to set an arbitrary (and subjective!) threshold, separating "good" from "bad" data. I love the way, in which it is done there, i.e. by grading sources by:

4: numbers that we can believe (e.g. births and deaths)

3: numbers that are reasonably accurate (e.g. well-designed & conducted surveys, e.g. the Natsal report)

2: numbers that could be out by quite a long way (e.g. non-uniform sampling, the Kinsey report)

1: numbers that are unreliable (e.g. surveys from newspapers, even with huge sample sizes)

0: numbers that have just been made up (e.g. "men think of sex every 7 seconds")

Just have a peek at the first chapter, which is freely accessible, and is exactly on data reporting, data reliability and dealing with subjective questions. A lot of thought is given about knowing the possible biases (e.g. people who are less likely to respond, who would like to downplay or exaggerate some things) and consistency of measurements.

So - in short: started reading to get curious facts about sex, ended up recommending to my data science students and mentees. (As the vast majority of problems starts with how you collects data, interpret it, and how well are you aware of its shortcomings).

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FabHKJul 2017

Or the well known phenomenon that when people are asked how many lifetime opposite-sex sex partners they've had, the average reported by men is about twice the average reported by women (though, by construction, they ought to be the same - just imagine a bipartite graph (undirected!) and count the lines going from left to right and compare with the number of lines going from right to left).

See eg. David Spiegelhalter, Sex by Numbers: What Statistics Can Tell Us About Sexual Behaviour, chapter 3.

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