Steve Accera asked me an interesting question a few months back. Why is 0.05 the cut-off for a p-value to be considered statistically significant?
This blog entry is courtesy of Fran Mayo who read an article in “Runner’s World” about some of the benefits of drinking pomegranate juice. It was fairly inevitable that I would get around to talking about pomegranate juice. It is, after all, all the rage right now and POM is currently in the media regarding some dubious health claims.
There are lots of reasons to drink pomegranate juice. Personally, when it first came out as a commercial product, I thought it was a pure novelty. I mean, have you ever EATEN a pomegranate? It takes FOREVER. The whole idea of juicing enough fruit to make a whole bottle of pure pomegranate juice was just unfathomable. So from my perspective, one of the reasons to drink pomegranate juice is because you can. All that pomegranate-y taste without the painstaking work.
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As with everything, more is not usually better. In statistics, more actually makes you more prone to being accidentally wrong (or, in statistics-lingo, spurious). Today, I’d like to talk about a basic concept that is taught to students in their introductory statistics courses (and hence, you would think, most researchers): The effect of multiple significance testing.
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