Dicing with Death: Chance, Risk, and Health

Statistics is the essential foundation for science-based medicine.  Unfortunately, it’s a confusing subject that invites errors and misunderstandings.  Non-statisticians could all benefit from learning more about statistics as well as trying to get a better understanding of just how much we don’t know. Most of us are not going to read a statistics textbook, but the book Dicing with Death: Chance, Risk, and Health by Stephen Senn is an excellent place to start or continue our education. Statistics, when used properly it is the indispensable discipline that allows scientists:

 …to translate information into knowledge. It tells us how to evaluate evidence, how to design experiments, how to turn data into decisions, how much credence should be given to whom to what and why, how to reckon chances and when to take them.

Senn covers the whole field of statistics, including Bayesian vs. frequentist approaches, significance tests, life tables, survival analysis, the problematic but still useful meta-analysis, prior probability, likelihood, coefficients of correlation, the generalizability of results, multivariate analysis, ethics, equipoise, and a multitude of other useful topics. He includes biographical notes about the often rather curious statisticians who developed the discipline. And while he includes some mathematics out of necessity, he helpfully stars the more technical sections and chapters so they can be skipped by readers who find mathematics painful. The book is full of examples from real-life medical applications, and it is funny enough to hold the reader’s interest.

What a Difference a Word Makes

Statistics (and probabilities) are frequently misunderstood, even by many scientists. Even what looks simple can turn out to be complicated and counter-intuitive.  Senn revisits an old question. If a man has 2 children and at least one of them is a boy, how likely is it that the other is a girl? Most people reason that there are only 2 possibilities, boy or girl, both equally likely, so there is a probability of 1 in 2, or 50%, that the other child is a girl. That’s wrong. In fact, there is a probability of 2 in 3: the other child is twice as likely to be a girl as a boy. The 50% answer is only true if you change the question slightly from “one of them is a boy” to “the firstborn is a boy.” If this doesn’t make sense to you, you really need to read the book…

Senn, in his book also talks about,

a. Does Medical Research Discriminate Against Women?

b. Applications Beyond Medicine

c. Statistics in the Courtroom

d. Statistics, MMR Vaccine, and Autism

e. Entertainment Value, and much more.

 

To know more, visit: https://sciencebasedmedicine.org/fun-with-statistics/

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