Right now, I’m pedaling really hard as I’m teaching a super-compressed (3 hours per day) math class for secondary-credential students. That’s my excuse for the slow-down in Bayes posts. The other being the ongoing problem that it takes me hours to write one of those; how Real Bloggers (the ones with more than about 6 readers) manage it I still do not understand.

So yesterday I dropped into my students’ morning class (physics) and heard the instructor (Dave Keeports) discuss the nature of science. Right up my alley, given my heartbreaking NSF project on teaching about the nature (and practice) of science. Also, the underlying logic of (Frequentist) statistical inference is a lot like the underlying logic of science (I’ve even written about it, e.g., here).

Anyway: Dave emphasized how you can never prove that a hypothesis is true, but that you can prove it false. Then he went on a little riff: suppose you have a hypothesis and you perform an experiment, and the results are just what your hypothesis predicts. Does that prove the hypothesis is true? (“No!” respond the students) Okay, so you do another experiment. Now do you know? (“No!”) But now you do a few dozen experiments, coming at the problem from different angles. Now do you know it’s true? (“No!”)

But wait—don’t you eventually get convinced that it’s probably true? He went on to talk about how, when we have a great body of evidence and general agreement, hypotheses can become “Laws,” and somewhere in there, we have coherent collections of hypotheses and data that warrant calling something a “theory,” at least in common parlance.

He didn’t stress this, but it was really interesting to see how he slid from firm logic to the introduction of opinion. After all, what constitutes enough evidence to consider a hypothesis *accepted*? It’s subjective. And it’s just like Bayesian inference, really just like our hypothesis about the coin: each additional head further cements our belief that the coin is double-headed, but it’s always *possible* that it was a fair coin.

Philosophers of science must have applied Bayesian reasoning to this issue.

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