The short answer
Retain the full family of outcomes and comparisons considered, the selection/stopping process and the validity of each underlying test. If a finite familywise-error method is appropriate, apply it to that declared family rather than just the favorable result. A small adjusted p-value neither measures effect size nor fixes biased data, dependence assumptions, missing outcomes or unsupported business claims.
What you’ll take away
- Count the family you explored, including outcomes that did not look favorable.
- Check test validity before applying a correction to its p-values.
- Keep exploratory selection, confirmatory evidence and business decisions separate.
Define what was searched before choosing the headline
A creator may compare several drafting changes across completion, clicks, reactions and multiple observation windows. Selecting the most favorable result from that search differs from one predeclared outcome at a fixed analysis time. Preserve the original question, all outcomes, exclusions and any repeated looks that shaped the decision.
Do not define the family as one test only after observing which result looked best. Record exploratory findings as candidates and keep an appropriate future confirmation separate. A test family is an inferential design question, not simply the number of rows visible in a dashboard or the number of posts you liked.
Check the actual inferential model first
A p-value concerns incompatibility with a specified model, not the probability that a content change works or the size of its benefit. The ASA’s 2016 statement summary emphasizes context and full reporting rather than a threshold-only decision. A corrected value from a test applied to the wrong unit remains the wrong analysis.
Check assignment, unit independence/dependence, outcome definition, missingness and any stopping/selection process. Different organic posts may have different audiences and observation ages. A family correction does not randomize those posts, repair a join, create missing customer outcomes or establish causal lift.
Reproduce a bounded familywise-error exercise
For a declared finite family of m valid tests, the Bonferroni union-bound reasoning allocates alpha/m to each test to bound familywise type-I error by alpha. This control does not require independence between tests, but it does require appropriate underlying test probabilities and a defensible family. NIST illustrates the general inequality in a specific ANOVA-contrast setting; this exercise does not claim that model describes LinkedIn activity.
In the constructed m=20, alpha=0.05 example, the per-test threshold is 0.0025. An entered p=0.01 does not meet it; the simple Bonferroni-adjusted value is min(1,20×0.01)=0.20. Separately, if 20 true-null tests were independent and each had type-I error 0.05, at least one false rejection would have probability 1−0.95^20, about 64.15%. That latter number explicitly depends on its independence assumption.
| Fictional raw p | Family size | Adjusted p | At alpha0.05 |
|---|---|---|---|
| 0.01 | 20 | 0.20 | Does not pass this procedure |
| 0.001 | 20 | 0.02 | Passes only under the stated model |
Resolve three comparison-family decisions
M1 — Constructed selected headline: twenty declared outcomes were checked, and only the smallest p=0.01 was shown as a winner. Decision: retain all twenty and report the family treatment; under the stated Bonferroni exercise it does not pass. Outcome: no selective single-test success, estimated effect or confirmed reader preference.
M2 — Constructed stronger candidate: p=0.001 in the same declared valid twenty-test family gives an adjusted value 0.02. Decision: retain its effect estimate, uncertainty, model limitations and practical decision separately. Outcome: a threshold result under the assumed procedure, not proof of a large benefit, a true hypothesis or a paying customer.
M3 — Constructed unknown search: metric definitions, windows and stopping choices changed repeatedly, with no complete family or valid underlying test documented. Decision: label the exercise exploratory and hold a confirmatory p-value claim. Outcome: multiplying the favorite value by a guessed test count does not certify the analysis.
Keep the method choice and practical consequence explicit
Bonferroni is a deliberately simple teaching method and can be conservative. Other familywise or false-discovery approaches answer different questions under their own conditions. Choose the appropriate analysis before seeking the smallest favorable output; this page does not select a universal statistical procedure for every creator dataset.
Report what you measured, the estimated difference and uncertainty, all relevant analysis choices and the bounded next content decision. A failed threshold is not proof of no effect; a passing one is not a measure of importance. An editorial change can also be justified by clearer information without pretending that this exercise measured a causal return.
Keep the complete family and reporting decision
The private record keeps original question, all outcomes/comparisons/windows, selection/stopping history, valid-test assumptions, declared family/alpha/method, effect/uncertainty and exploratory or confirmatory wording. The packet reproduces the constructed arithmetic without running a statistical test on real people or public posts.
The NIST general-inequality section and the ASA’s official 2016 statement summary were read on 1 November 2026. No current creator effect, population, participant preference or business outcome was measured. Use the actual complete comparison plan; these examples provide no observed test or confirmed content win.
Sources & product references
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