Does regression to the mean explain successful diet programs?

We might remember ‘regression to the mean‘ from those lists of threats to validity (in terms of causal analysis). But when is it actually likely to be a problem for genuine evaluation? In a recent post by Rebecca Goldin on the stats.org blog, “Why any ol’ diet will work (if your BMI is high enough):

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Critiquing a partial evaluation – is a half-full glass better or worse than no drink at all?

Where do you draw the balance? Should we stop doing small evaluations that only look at a few pieces of data, to avoid the risk of misinterpretation? Or should we work harder to ensure their findings can be appropriately incorporated with other information?

In a recent post on the George Mason University website www.stats.org ,

Read the whole post –> Critiquing a partial evaluation – is a half-full glass better or worse than no drink at all?

Why genuine evaluation must be value-based

Every now and then the question is raised about whether evaluation really needs to incorporate “values”. Can’t we just measure what needs to be measured, talk to the right people, pass on whatever they (the people and the data) say? Why is there a need to dig into the messiness of “value”? Do we really need to say anything about how “substantial” or “valuable” an outcome is in the scheme of things, whether an identified weakness is minor or serious, whether implementation has been botched or aced, whether the entire program is heinously expensive or an incredible bargain given what it achieves? Do we really need to do anything seriously evaluative in our evaluation work? Yes, we do. And here’s why …

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