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Book Review of “Thinking in Bets”, Part 1 of 2

Written by Annie “The Duchess of Poker” Duke, Thinking in Bets is a national bestseller, and for good reason. The writing style is direct and to-the-point, and the advice is motivated by concrete examples taken from the author’s own experience. For instance, one anecdote concerns a bet among a group of friends on whether or not one of them, “Ira the Whale”, could eat 100 White Castle burgers in a single sitting. David Grey, one of the author’s friends, bet $200 on the Whale:
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Redefine Statistical Significance XVIII: A Shockingly Honest Counterargument

Background: the 2018 article “Redefine Statistical Significance” suggested that it is prudent to treat p-values just below .05 with a grain of salt, as such p-values provide only weak evidence against the null. By threatening the status quo, this modest proposal ruffled some feathers and elicited a number of counterarguments. As discussed in this series of posts, none of these counterarguments pass muster. Recently, however, Johnson et al. (in press, Injury) presented an empirical counterargument that we believe is new. This counterargument is brutally honest and somewhat shocking (to us, anyway).

Johnson and colleagues start off by defining the p-value and its purpose:

“The primary purpose of using a P value is to minimize type I errors — erroneous conclusions made about differences between groups when no such difference truly exists. The type I error rate is often specified a priori at 0.05, meaning that there is a 1 in 20 chance — or a 5% risk — that the difference detected is because of chance rather than attributed to the effects of the intervention.”

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