This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1 · go to the accepted answer.
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.
Worth saying I have only my own numbers here, and n is small.
Where I have landed on Measurement error in home scales, having got it wrong once in public: the direction is clear, the magnitude is not, and anyone quoting a precise magnitude has borrowed it from somewhere that did not measure 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.
Stating my assumptions rather than smuggling them in.
Answering the Measurement error in home scales question as asked, then the question I think is meant. As asked: yes, with the qualification below. As meant: it depends on how the first measurement was taken.
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.
Take it as a starting point and not as a specification.
On Measurement error in home scales: the maintained page in the documentation commons covers the general case with citations and a review date, which is more reliable than any reply here including this one.
Same experience here, different supplier, so it is at least not unique to one of them.
Percentages of small denominators should be reported with the denominator. Two out of three is not sixty-seven per cent in any useful sense.
I would treat that as a working assumption and revisit it.
Coming back to post #70, because the follow-up matters more than the original answer.
Where an analysis was changed after seeing the data, the honest thing is to report both and say which was pre-specified.
Seconded. It reads as careful rather than confident, which is the right register.
Adding a null result on Measurement error in home scales. I looked, carefully, and found nothing, and null results deserve posting precisely because they never are.
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.
I had written a reply contradicting post #73 and deleted it. Here is what survived.
Measurement error in home scales sits at the boundary between what this community can usefully discuss and what it cannot, and I think it falls on the discussable side, narrowly.
Confirming post #73 from a second method, which matters more than confirming it from a second person.
Trying to state the Measurement error in home scales position in a way that someone who disagrees would recognise as fair, because I do not think the version in this thread passes that test.
Rounding and significant figures carry information about precision. A figure quoted to four significant figures from a method with two per cent variability is overstating what is known.
The confident version of this sentence would be wrong, so here is the hedged one.
An update on my earlier Measurement error in home scales post: the pattern held for another six weeks and then stopped, which I did not predict and cannot explain.
Narrowing post #77, because the general version has more than one answer.
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.
The answer changed when I changed how I was measuring, which was informative.
My position on Measurement error in home scales is current rather than settled. I have revised it once already and I expect to again, so treat it accordingly.
Offering a way to settle Measurement error in home scales rather than another opinion about it. Two measurements, taken the same way, a fortnight apart. If the difference is within the noise, the question was not answerable at this precision.
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 would hold that lightly until someone with a larger sample weighs in.
Answering the question post #80 raises rather than the one it answers.
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.
Two sources, same conclusion, and I could not rule out that one copied the other.
Second-hand on Measurement error in home scales, so weight it accordingly — someone whose method I trust told me this and I have not verified it myself.
The bit of Measurement error in home scales that nobody enjoys is that the answer changes depending on what you are trying to decide with it. Say what the decision is and the thread will converge.
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.
This follows post #86 rather than contradicting it.
Survivorship in a self-reporting population biases every aggregate produced from it, and the bias is in the flattering direction.
The uncertainty is in the assumption, not in the calculation.
Worth separating two things that post #88 runs together.
Reframing Measurement error in home scales slightly, because I think the disagreement is about the question rather than the answer. If the question is "does it happen", yes. If it is "how often", nobody here knows.