The Gift That Keeps on Giving: The p-value

Naman Mishra, a friend and a junior from the Gokhale Institute, was kind enough to read and comment on my post about Abhinav Bindra and the p-value. Even better, he had a little “gift” for me – a post written by somebody else about the p-value:

P values are the probability of observing a sample statistic that is at least as different from the null hypothesis as your sample statistic when you assume that the null hypothesis is true. That’s a pretty convoluted but technically correct definition—and I’ll come back it later on!

It is convoluted, of course, but that’s not a criticism of the author. It is, instead, an acknowledgement of how difficult this concept is.

So difficult, in fact, that even statisticians have trouble explaining the concept. (Not, I should be clear, understanding it. Explaining it, and there’s a world of a difference).

Well, you have my explanation up there in the Abhinav Bindra post, and hopefully it works for you, but here is the problem with the p-value in terms of not how difficult the concept i, but rather in terms of its limitations:

We want to know if results are right, but a p-value doesn’t measure that. It can’t tell you the magnitude of an effect, the strength of the evidence or the probability that the finding was the result of chance.

In other words, the p-value is not the probability of rejecting the null when it is true. And here’s where it gets really complicated. I myself have in classes told people that the lower the p-value, the safer you should fail in rejecting the null hypothesis! And that’s not incorrect, and it’s not wrong… but well, it ain’t right either.

Consider these two paragraphs, each from the same blogpost:

But also, there’s this, from earlier on in the same blogpost:

This.”, you can practically hear generation after generation of statistics students say with righteous anger. “This is why statistics makes no sense.”

“Boss, which is it? Can p-values help you reject the null hypothesis, or not?”

Fair question.

Here’s the answer: no.

P-values cannot help you reject the null hypothesis.

You knew there was a “but”, didn’t you? You knew it was coming, didn’t you? Well, congratulations, you’re right. Here goes.

But they’re used to reject the null anyway.

Why, you ask?

Well, because of four people. And because of beer and tea. And other odds and ends, and what a story it is.

And so we’ll talk about beer, and tea and other odds and ends over the days to come.

But as with all good things, let’s begin with the beer. And with the t*!

*I’ve wanted to crack a stats based dad joke forever. Yay.

One thought on “The Gift That Keeps on Giving: The p-value

  1. Thanks for writing about the most debated and confusing word, “P-value”.
    In my opinion, the p-value can be used to reject or not reject the null hypothesis, given that the research design does not have a glaring error.  In social science, nothing is fixed for any problem. You can not say if the p-value is lower than or higher than a specific number, the hypothesis can be rejected or not rejected always. The decision lies in the understanding of the problem and the objective and scope of the problem. 

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