Correlation in a self-tracked dataset: what it can support

P-values and significance: p<0.05 means the data would be surprising if the null hypothesis were true, not that the null hypothesis is false. A non-significant p-value does not mean "no effect".

Two sources, same conclusion, and I could not rule out that one copied the other.

Taking post #8 at face value and following it one step further.

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.

Two people in this thread mean different things by correlation and are disagreeing about the definition while believing they are disagreeing about the facts. Worth pausing to define it.

Percentages of small denominators should be reported with the denominator. Two out of three is not sixty-seven per cent in any useful sense.

For what it is worth, the same held on the two occasions I checked.

The honest answer on correlation is that it depends, and the useful part is the list of what it depends on. Four items, in rough order of how much they matter.

Most people get the first two right and then argue about the fourth.

Post #91 answers the question as asked. The question underneath it is different.

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.

Same experience here, different supplier, so it is at least not unique to one of them.

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.

On correlation, 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.