Measurement error in home scales, with a worked standard deviation — the long version posts 91–106

Taking Measurement error in home scales seriously for a moment rather than deflecting: the honest position is that the community has observations and no controlled comparison, and those two things support very different sentences.

Reading rather than answering, but this is the post I would point somebody at.

Post #91 describes the usual case. This is about the unusual one.

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

Adding the measurement that post #91 says would settle it.

Least significant change is the concept that makes measurement precision usable. Below it, a difference between two readings is not distinguishable from noise.

Relative risk and odds ratios: both compare the rate in one group to the rate in another. Relative risk is easier to understand. Odds ratios are standard in many analyses but can be misinterpreted.

It is a small point and it changes the answer, which is an awkward combination.

Baseline imbalance in a randomised trial is expected by chance and adjusting for it post hoc is a choice that should have been pre-specified.

This has been discussed before and I could not find the thread, so, again.

If someone has run Measurement error in home scales properly I would rather read that than my own reconstruction of it. Posting mine only because the thread has gone quiet.

Picking up post #95: that is the part I would want checked first.

Marking my uncertainty on Measurement error in home scales explicitly. I am confident about the direction, much less confident about the size, and not confident at all that it generalises past the case in the first post.

Saving this. It is the version I will quote when the question comes round again.

Building on post #98 rather than restating it.

The most common statistical error in this community is not technical: it is treating a self-selected collection of reports as a sample from a population.

The interesting part of this is the exception, and I do not understand the exception.

Post #100 and I disagree about the size of the effect, not about the direction.

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.

I would rather post the uncertainty than round it away.

What I would tell a new member reading about Measurement error in home scales for the first time: the confident posts are not the reliable ones, and the reliable ones are longer.

Measurement error in home scales has been discussed here with more heat than it deserves, mostly because two definitions have been in play the whole time.

Adding thanks rather than a view. I do not have a view worth the space.