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Twelve metrics on the dashboard, and one just turned green. Is that a real win, or is that just what happens when you check twelve things at once?
What you'll have at the end
A short list of which flagged metrics are still significant once the correction is applied
You need
A dashboard that already runs its own significance test on every metric it tracks against a stated baseline and records a raw p-value, or a red/green flag, for every one of them this cycle, including the ones that never changed color.
Not covered
Running the underlying significance test for any one metric on its own, a t-test, a z-test, or otherwise; this starts once you already have a raw p-value for each metric in hand.
Compare two groups' averages with a t-test
go there first if you don't yet have a raw p-value for each metric on your own dashboard.
Find which group differs with a Tukey post-hoc test
use Tukey instead when your comparisons are correlated pairwise averages from one ANOVA, not independent metrics like these twelve.
Checked 26 Aug 2026
Part of the Statistics Fundamentals cookbook