P-values should be banned. Every use of them involves a fallacy or mistake in thinking.

“P-values have some good uses.”

No, they don’t. I used every as in every.

“P-values are fine if used properly.”

I’m not getting across. P-values have no proper use.

“P-values have some good uses.”

I wrote two papers with about a dozen or two arguments proving my contention that every use of a p-value is fallacious or mistaken. Here is one, here is the other.

“P-values are fine if used properly.”

Did you read the papers?

“P-values have some good uses.”

Which of the arguments do think is flawed, and how is it flawed?

“P-values are fine if used properly.”

So you’re saying you didn’t read the papers, or that perhaps you scanned them hurriedly, or that you did read them but can discover no flaws in the arguments. Right?

“P-values have some good uses.”

What you’re trying to say is that, even though it’s been proved p-values are fallacious and mistakes, that they have good uses, as long as those uses are proper?

“Yes. P-values are fine if used properly.”

It’s not only p-values that have to go. Parameter-centric methods cause vast, mighty over-certainty.

“Everybody uses parameter-based methods.”

The idea is that people substitute certainty they have in parameters, which do not exist and which therefore are of no interest to man or beast, with certainty in observables.

“Everybody uses parameter-based methods.”

People start all analyses by asking about what happens to an observable—what happens to the uncertainty in its value, that is. They say, “If we change this X, how does it affect our uncertainty in Y?” Grand question, that. But they end by saying, “The parameter in this model take this value, plus or minus something.” What does that have to do with the price of cookies in Byzantium?

“Everybody uses parameter-based methods.”

If we changed practice and eliminated all parameter-based methods, then we’d have a much better understanding of how much we don’t know. We couldn’t then go around so cocky and claim we knew much more than we really do.

“Everybody uses parameter-based methods.”

It’s worse. For if these non-existent parameters take certain values, cause is said to have been discovered. This is the curse of null hypothesis significance testing.

“P-values are fine if used properly.”

Everybody says “Correlation is not causation.” Every authority swears to this, and for good reason. It is true. It is as solid a piece of philosophy as we have in science. Yet if a p is wee, correlation becomes causation.

“P-values have some good uses.”

It’s not only frequentists, of course. So-called Bayesians with their Bayes factors commit the same fallacy at the same rate as frequentists.

“Bayes factors are well accepted.”

Same rate as frequentists. Nothing? That’s a joke, son.

“Bayes factors are well accepted.”

Say it. Say it with me: correlation isn’t causation.

“Bayes factors are well accepted.”

Correlation isn’t causation when the p is wee.

“P-values are fine if used properly.”

Correlation isn’t causation when the Bayes factor is big, either.

“Bayes factors are well accepted.”

Tell me. If correlation isn’t causation, then just what does it mean when a p is wee? What has been proved? If the Bayes factor is a whopper, what does it mean, exactly? Not in terms of a model, but of reality. Of the observable. Of cause.

“Statistical significance has been reached.”

And what does “statistical significance” mean except that it is a restatement the p-value was wee?

“P-values have some good uses.”

You’ve read this award-eligible book, yes? Now at a very affordable $40, or thereabouts. That magnificent work has a long and detailed discussion of cause, of why probability models can’t identify cause. Of what cause means. What do you say to those arguments?

“P-values are fine if used properly.”

So you’re saying everything is fine, that nothing need change. That the philosophy of probability infecting statistics now is not only benign but beneficial. That we needn’t answer any of these hard questions about cause and probability. Right?

“Who are you anyway? Just some guy on the internet.”

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