I’ve been reading about how some studies that we assume provide us with information about the world actually don’t teach us anything because of statistical manipulation that makes them look more relevant than they are. How does this work?
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“p-hacking” is a term used to describe intentional misuse of data to try and push a certain point or narrative.
Basically, say you collect 1,000 data points in a study, the first 500 don’t really support your idea, but the second 500 do. Well, if you want to publish an article about your point, what if you simply ignore the first 500 data points, never tell anyone about them, and only include the second 500. Boom now it seems like your idea is supported.
That is a really simple example of manipulating your data where overall the data doesn’t tell you anything significant, but you can make it look like it does.
And there are like half a dozen other ways to kind of achieve this.
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