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.
Topic summary
Measurement error in home scales, with a worked standard deviation — the long version
This is a generated summary. It shows the 9 most-liked posts from a topic of 106, in their original order, with the accepted answer included where one exists. It is a reading aid and it will miss nuance — the full topic is the record.
d.magalhes, post #9: 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. Go to post
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.
Post #33 put the caveat in the right place and I want to underline it.
Practical note on Measurement error in home scales: write down what you expect before you look. The number of times I have found what I went looking for is higher than chance would allow.
29 likes 5mo
Adding a small correction to the Measurement error in home scales summary above rather than a disagreement with it. The substance holds; one of the figures is out by a factor that matters.
27 likes 5mo
Confirming post #53 from a second method, which matters more than confirming it from a second person.
Survivorship in a self-reporting population biases every aggregate produced from it, and the bias is in the flattering direction.
Two people can read the same figure differently here and both be reasonable.
27 likes 4mo
This follows post #73 rather than contradicting it.
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.
31 likes 4mo
Post #84 describes the usual case. This is about the unusual one.
Effect sizes: the magnitude of a difference, not just whether it is statistically significant. A difference that is significant (p<0.05) might be too small to matter. A large effect might not be significant if sample size is small.
Reading it back, the second half matters more than the first.
31 likes 3mo
e.almeida, post #48: One caution on Measurement error in home scales: everything above assumes the underlying documentation is what it claims to be. That assumption is doing real work and is rarely stated. Go to post
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.
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- [2026 update] Correlation in a self-tracked dataset: what it can supportResearch Methods › Statistics · 2 replies
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