Since Measurement error in home scales keeps coming up, it should probably be a maintained page rather than a recurring thread. I am happy to draft it if someone with more direct experience will review it.
On the subject in the title: Measurement error in home scales, with a worked standard deviation — the long version Working notes rather than a conclusion.
A narrow question about Measurement error in home scales, deliberately narrow, because the broad version has been asked here four times and produced four long threads and no answer.
One question, stated units, stated method, and what I have already ruled out.
The opening post and I disagree about the size of the effect, not about the direction.
A note on how Measurement error in home scales gets discussed rather than on Measurement error in home scales itself: the confident posts get the replies and the careful ones get ignored, and the careful ones have been right more often.
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.
Not the answer, but possibly the question that gets there.
Confounding: a third variable explains an apparent association. In randomised data, randomisation balances confounders. In observational data, confounders can be adjusted for but unknown ones cannot.
I have kept the units in throughout, for the obvious reason.
Building on post #2 rather than restating it.
The arithmetic on Measurement error in home scales is the easy part and it is where the errors are, which is an uncomfortable combination. Show your working and someone will catch it.
Where an analysis was changed after seeing the data, the honest thing is to report both and say which was pre-specified.
Small point, but it is the one that usually catches people.
Understood. Thank you for being specific about the limits of it.
Since Measurement error in home scales keeps coming up, it should probably be a maintained page rather than a recurring thread. I am happy to draft it if someone with more direct experience will review it.
Answering the question post #7 raises rather than the one it answers.
The version of Measurement error in home scales that circulates here is a simplification of a simplification. It is not wrong, but it has lost the conditions under which it holds, and those conditions are where the interesting cases live.
Marking my place. If it changes for me I will come back and say so.
A methods point on Measurement error in home scales rather than a substantive one: if the comparison is not like for like, the difference you are measuring is the difference in method.
Adding a reference point for Measurement error in home scales. Mine is a single case, collected without controls, and I am posting the method alongside it so it can be discounted appropriately.
Where I part company with post #15, and it is a narrow parting.
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.
I would want the raw data before agreeing with my own summary of it.
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.
The variance between people here is larger than the effect being discussed.
Two claims get bundled together under Measurement error in home scales and they need separating. The descriptive one — this is what was observed — is usually well supported. The causal one — this is why — usually is not.
Almost every disagreement in threads like this one dissolves once you say which of the two you are making.
Confidence intervals: rather than a single point estimate, a range of plausible values. A narrow interval means precise measurement; a wide interval means measurement is imprecise. Wider intervals (more uncertainty) are honest about limitation.
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.
This is the sort of exchange that makes the archive worth searching.
I had written a reply contradicting post #22 and deleted it. Here is what survived.
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.
It is the sort of thing that seems obvious in retrospect and was not at the time.
Where the Measurement error in home scales reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.
A note on scope: what I am saying about Measurement error in home scales applies to the case in the first post and I would not extend it further without checking.
Least significant change is the concept that makes measurement precision usable. Below it, a difference between two readings is not distinguishable from noise.
If it helps: the failure mode here is usually boring rather than dramatic.