# Abhinav Bindra and the p-value

How would you explain what the p-value is to somebody who is new to statistics?

There is an argument to be made about whether the p-value should be explained at all, regardless of how well you know statistics. And we will get to those ruminations too. But for the moment, think about the fact that you want to get a person familiar with how statistics is “done” today. This person doesn’t wish to become a professional statistician, but is very much a person who is interested in statistics.

Maybe because they have team members who work in this area, and this person would like to understand what they’re talking about in their presentations. Maybe because they are interested in statistics, but in a passively curious fashion (the very best kind, if you ask me). Maybe because it is a part of their coursework. Maybe because they’re training to be economists/psychologists/sociologists/anthropologists/insert-profession-here, and statistics is something they ought to be familiar with. Whatever the reason, they want to know how to think about statistics, and they want to know how to understand the output of various statistical programmes.

And sooner or later, this person will come across the word “p-value”. Or maybe they will see “Significance” in some statistical software’s output. Or worse, “sig.” And they might want to know, what is this p-value? This blogpost is for folks who’ve asked this question.

Here’s how I explain the intuition behind p-values to my students in statistics classes.

“Imagine”, I tell them, “that Abhinav Bindra is standing at the back of the class”.

Memories are fickle, and increasingly, over the last three to four years, I then have to explain who Abhinav Bindra is.

“So, ok”, I continue, once we’re back on track, “Abhinav is standing at the back of the class. He’s got his rifle with him, and he’s going to aim at this “x” that I’ve drawn on the board.

And so Abhinav begins to shoot, aiming at the large x. But something interesting happens. Instead of hitting the large X, as one would expect from a world-class shooter, he ends up aiming almost exclusively towards the right of the blackboard. Like so:

Those “x”-es that you see towards the right are to be thought of as bullet holes, and I will not be taking any questions about my artistic skills. The first picture was created by Dall-E, and the second picture may or may not have been edited by me in MS-Paint. Like I said, no further questions.

So, anyways, we should all be a little bit gobsmacked, right? Here we are, hoping to watch a world-class rifle shooter at work, and he ends up shooting most of the times very, very far away from the intended target.

What should we make of this fact?

Let’s think about this. We know, from having seen our fair share of these competitions on TV and on the internet, that a world class champion really should be shooting better. We know that conditions inside the classroom aren’t so windy that it might be a factor. We know that he isn’t distracted, we know that his rifle is working well, and we know that the classroom isn’t so big as for it to be a problem.

Well, we don’t “know” all of these things, but I’m going to assume them to be true, and I invite you to join me in this little thought experiment.

Then what do we conclude about the fact that all the little “x”‘es are very far away from the big “X”?

Maybe – and we all have a suspicious gleam in our eyes as we turn in accusing fashion at the rifle shooter – this guy at the back of our classroom isn’t Abhinav Bindra, but some impostor?

That is, so unlikely is the data in front of our eyes, that we have no choice but to question our assumptions. The data, you see, is there – right there – in front of your eyes. You’ve checked and rechecked those little x’es, and you’ve eliminated all other possible explanations. And so you’re left with but one inescapable conclusion.

So far away are the little x’es from the big X, that we can’t help but declare the guy to be an impostor.

“The p-value is the probability of getting a result as extreme as (or more extreme than) what you observed, given that the null hypothesis is true.”

Those little x’es, they’re very extreme. If we assume that it is Abhinav Bindra, what is the probability that he will shoot that far away from the intended target? 50% probability that he will aim shoot that badly? 30%? 20%? 10%? Less than 5%?

And if the probability that he will shoot that badly is less than 5%, then maybe we shouldn’t be assuming that it is Abhinav Bindra in the first place?

The TMKK of the p-value is that it gives us a way to reject the null hypothesis. Why do we need to reject the null hypothesis? We don’t “need” to reject the null hypothesis, but keep in mind that we usually formulate the null hypothesis to mean that there has been “no impact”. That rain has “no impact” on crops. That Complan has “no impact” on the growth of heights. That a new pill has “no impact” upon the health of an individual. That this blogpost has had “no impact” on your understanding of the p-value.

And we design an experiment in which we see if crops grow after it rains, if heights grow after kids have Complan, if people feel better after they take that pill, and if you guys now think you’ve got a handle on what the p-value is, after having read all this.

Designing an experiment is a separate hell in and of itself, and we’ll take a trip down that rabbit hole later.

But in each of these experiments, if we see that crops have sprouted magnificently post the rains, if we see that kids have shot up like beanstalks after they’ve had Complan, if people have made miraculous recoveries after taking that pill, and if we see EFE readers strut around like peacocks around stats professors – well then, what should we conclude?

We should conclude that our null hypothesis was wrong.

A low enough p-value allows us to be reasonably confident that our null hypothesis wasn’t correct, and that we should reject it.

Got that?

Congratulations, that’s the good news.