Second pass at: Regression to the mean in progress reports posts 61–76

No disagreement from me. Posting only so the question does not look ignored.

What I would check first on Regression is whether the thing being measured moved or whether the way of measuring it moved. Those look identical in a graph.

A standard deviation describes the spread of individuals and a standard error describes the precision of the mean. Quoting one where the other belongs changes the apparent result substantially.

That distinction has done more work for me than anything else in this category.

Picking up post #62: that is the part I would want checked first.

Regression was covered in the wiki last year and the page has a review date on it, which is a better starting point than my memory of a thread.

Regression to the mean: if you select people with extreme values (very high or very low), their next measurement is often less extreme just by chance. This can look like a treatment effect when it is just statistics.

Flagging that the sources on this are thinner than the confidence in the thread suggests.

Worth separating two things that post #65 runs together.

A definition problem is doing most of the work in this Regression discussion. Once the term is pinned down I suspect the disagreement mostly goes away and what is left is small.

This follows post #65 rather than contradicting it.

Bayesian and frequentist analyses answer different questions and both are legitimate. What matters is that the reader knows which is on offer.

I have been on both sides of the Regression argument in this category within eighteen months, which should tell you how strong the evidence for either side is.

Adding a note of thanks rather than an opinion. I did not know most of that.

Post #68 is the version of this I will quote in future. One addition.

Where the Regression reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.

Where I part company with post #71, and it is a narrow parting.

A p-value is the probability of data at least this extreme given the null hypothesis. It is not the probability the hypothesis is false, and almost every plain-language gloss gets that backwards.

Adding a null result on Regression. I looked, carefully, and found nothing, and null results deserve posting precisely because they never are.

I think the Regression question is answerable and has not been answered, which is a more optimistic position than most of this thread.