Statistics, experimentation, causal reasoning · insight-0015
Treat a p-value just under 0.05 as weak evidence that needs replication, not a verdict
- Evidence type
- documented fact
- Difficulty
- easy
- Roles
- Data scientist, Data analyst
- Industries
- cross-industry
Summary: situation, action, reason, boundary
Situation
A test ends at p = 0.04 on the primary metric and the readout says "statistically significant, ship it".
Action
Report the interval and effect size, compare them with the decision threshold, and weigh prior evidence from similar changes. For costly or irreversible decisions, replicate or run a confirmation period before scaling.
Reason
A p-value measures incompatibility with a null model, not the probability the effect is real, and it varies widely between replications of the same design.
Boundary
Experiments, model comparisons and observational analyses that feed investment decisions.
Symptom or risk
A borderline result carries little evidence on its own; with many tests per quarter, a share of such wins are false, and the team builds roadmaps on them.
Exceptions
Cheap, reversible changes where a false positive costs little can ship on weaker evidence with monitoring.
Trade-offs
Replication slows the cadence; skipping it spends credibility.
Pitfalls
Re-running only the borderline-positive tests, which biases the portfolio toward chance wins.
Lessons and concepts
Claims and their sources
A p-value does not measure the probability that the studied hypothesis is true, and a scientific or business decision should not rest only on whether it passes a threshold.
Claiming success with a borderline p-value is listed among common metric interpretation pitfalls in online experiments.