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.
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.
Not a strong opinion, just a consistent one.
I changed my mind about Measurement error in home scales after someone here asked me for the source and I could not produce one. That is worth saying out loud because it is the ordinary way it happens.
Post #29 and I disagree about the size of the effect, not about the direction.
Bayesian and frequentist analyses answer different questions and both are legitimate. What matters is that the reader knows which is on offer.
Taking post #33 at face value and following it one step further.
The useful distinction on Measurement error in home scales is between what was measured and what was inferred from it. Both end up in the same sentence and only one of them has error bars.
Source for the Measurement error in home scales figure, since it was asked for. It is in the discussion rather than the abstract, which is why the version circulating is stronger than the paper is.
Reading the surrounding paragraph is worth the two minutes. The authors are more careful than their summarisers.
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.
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.
Following this. I have the same question and no better information than the first post.
I had written a reply contradicting post #37 and deleted it. Here is what survived.
For anyone finding this later: the short answer on Measurement error in home scales is that it depends on one thing, and the rest of the thread is people identifying which thing.
Confirming post #37 from a second method, which matters more than confirming it from a second person.
Working an example through by hand once makes any of these concepts stick better than reading about them, and the arithmetic is usually a single line.
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.
Measurement error in home scales is a good example of a question where the honest answer is boring and the interesting answers are unsupported. I would go with boring.
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.
Post #40 describes the usual case. This is about the unusual one.
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.
It is worth stating the boring hypothesis before the interesting one.
Building on post #42 rather than restating it.
On Measurement error in home scales, I would rather understate and be corrected upward than overstate and be quoted. That is a house style here and it is a good one.
Whatever the answer on Measurement error in home scales turns out to be, the method for getting there is the same: state the assumption, do the arithmetic in public, invite the correction.
Taking post #46 at face value and following it one step further.
Two questions I would want answered before drawing anything from the Measurement error in home scales data above: how were the cases selected, and what happened to the ones that dropped out.
Post #48 and I disagree about the size of the effect, not about the direction.
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.
Written from notes rather than memory, which is why the numbers are specific.
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.
On post #51 — agreed on the reasoning, with one qualification.
Multiple testing inflates the false-positive rate in a way that is entirely predictable and entirely correctable. The correction should be declared in advance.
One of those cases where knowing the mechanism does not help the decision.
Thank you for taking the time. That was more work than a reply usually is.
On Measurement error in home scales I would separate what is worth knowing from what is worth acting on. The first list is long and the second is short, and conflating them is how threads get heated.
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.
Where I part company with post #56, and it is a narrow parting.
Number needed to treat: how many people need to be treated to prevent one bad outcome or achieve one good outcome. More intuitive than relative risk reduction.
Flagging that the sources on this are thinner than the confidence in the thread suggests.
Post #59 is the version of this I will quote in future. One addition.
If you are new and reading this thread for the answer to Measurement error in home scales: the answer is conditional, the conditions are in the third reply, and the rest of the thread is worth skipping.