An interval read beside something else
Five times in six
A 95% interval is read as a 95% chance that a replication's estimate will land inside it. With the spread known and a replication of the same size, the chance is 83.42% — five times in six — because both estimates are uncertain. An original that landed two standard errors from the truth captures a replication 48.40% of the time; a replication a tenth the size lands inside 44.54% of the time; and among significant originals from studies with 17% power, 66.94%.
Two intervals that overlap
Two 95% intervals that just touch are read as a difference at the edge of significance. With equal standard errors their difference has p = 0.0056, not 0.05; two intervals can overlap by 58.6% of an arm and still differ at exactly 5%; standard-error bars that just touch mark p = 0.157; and when the two estimates are correlated at 0.8, touching intervals conceal a difference of 6.2 standard errors. Read as a test, non-overlap needs 1.66 times the sample for the same power.
Significant in one, not in the other
Two studies of exactly the same effect, each with 50% power, disagree about significance half the time — and when they do, the test of the difference between them is significant in 9.75% of cases. A p of 0.01 beside a p of 0.20 is a difference with p = 0.36. Among four subgroups sharing one effect, at least one significant and one not happens 87.5% of the time, and the test that would tell a real difference apart needs four times the sample the effect itself needed.
The verdicts watch the threshold, not the gap
Two studies whose effects really differ are read through their verdicts as often as through their difference. Halves of an 80%-power study whose effects differ by the whole effect split 72.87% of the time against 49.99% when they do not — a split is never more than 1.86 times as likely under any difference up to twice the effect. And a halving of the effect in two large studies leaves both significant 97.93% of the time, while the test of the difference finds it 80.74%.
A subgroup inside its own trial
A trial with 80% power, significant overall and not in the fifth of it that is women, has told the reader almost nothing about women: that pattern turns up 57.45% of the time when women have the full effect and 58.26% when they have none. The subgroup is part of the whole, so the two verdicts are correlated, and the one test that answers the question — the subgroup against everyone else — can be recovered from the two printed intervals and nothing more.
Sixteen subgroups and one effect
A trial of four hundred with 80% power tabulates its result by eight binary characteristics — sixteen subgroups — and the treatment's effect is the same for every patient. Among trials significant overall, 93.5% report at least one subgroup where it is not, and the median one reports seven. Some subgroup-against-the-rest interaction reaches 5% in 34.6% of trials — eight independent chances — and when the characteristics overlap as two age thresholds do, 18.6%, about four. The effective count is set by the correlation between the characteristics' split indicators, (2/π) arcsin of their latent correlation, and a Bonferroni reading of the eight holds the rate at 5% whatever the overlap.
One characteristic that really matters
A trial of four hundred tabulates eight characteristics and one of them genuinely changes the treatment's effect — the whole effect on one side, none on the other. Tested alone at 5% the real interaction is found 81.0% of the time; at the Bonferroni threshold that protects the table, 53.6%. It is the table's largest interaction in 82.4% of trials when the characteristics are independent and in 54.4% when they overlap like nested age bands, and in that case another characteristic crosses the Bonferroni line in half of all trials and is the top finding in a quarter. A family-wise threshold counts false rows; it cannot say which of two overlapping rows is the cause.
The pattern a cause leaves on its neighbours
A characteristic that modifies a treatment's effect raises every characteristic that overlaps it, in proportion to the overlap, and fitting that pattern was supposed to say which one is the cause. Fitted with the noise's own correlation it names the largest row on every trial, because the noise draws the same pattern — so the 54.4% at which the largest row is right among nested characteristics is the most any reading of the table can manage, and the honest answer is a set of about five.