Treat statistical significance in medicine as one signal—pair it with effect size, confidence intervals, design quality, and clinical meaning.
Readers search for a crisp way to handle statistical significance in medicine. The answer is not a magic number. Researchers and peer reviewers should treat a p-value or a threshold crossing as one piece of evidence within a bigger judgment. That judgment draws on effect size, precision, prespecified plans, bias checks, and whether the result changes care. This guide lays out a practical path that matches guidance from major editors and statisticians.
How Should Researchers And Peer Reviewers Interpret Statistical Significance In Medicine: A Practical View
Start with the question, the endpoint, and the patients. Then read the estimate. Ask what the number means for a real person. Look at the interval around that estimate. Check whether the analysis matched the protocol. Only then ask whether the test reached a chosen alpha level. That order reduces mistakes and keeps the spotlight on care.
Use the points below during design, writing, and peer review:
- Lead with effect size and a confidence interval that maps to the decision.
- Report the exact p-value without asterisks or traffic-light labels.
- State the alpha, the analysis set, and any changes from the plan.
- Connect the numbers to a clinical threshold such as a minimal clinically important difference.
- Explain uncertainty, including issues like multiplicity, missing data, and early stopping.
Common Findings And What They Mean
| Report Language | What It Should Mean | What It Does Not Mean |
|---|---|---|
| p = 0.04; alpha = 0.05 | The result met the prespecified threshold; the data are less compatible with the null than with the alternative under the model. | Proof of a true effect, or a guarantee of reproducibility. |
| p = 0.20 | The data did not reach the threshold; the study may lack power or the effect may be small or absent. | Evidence that the treatments are identical or that no effect exists. |
| 95% CI excludes the null value | Range of compatible effects does not include the null under the model. | Assurance that the true effect equals the point estimate. |
| 95% CI includes the null value | Data allow both benefit and no effect (or harm); more data or better design may be needed. | Proof of no effect. |
| Primary endpoint met the threshold | Claim rests on a plan set in advance. | License to ignore safety signals or secondary outcomes. |
| Exploratory subgroup met the threshold | Hypothesis-generating; needs confirmation. | Definitive evidence for that subgroup. |
| Non-inferiority margin met | Treatment effect stayed within the allowed loss. | Superiority over the control. |
| Equivalence bounds met | Both margins satisfied within a prespecified range. | Superiority or better outcomes on every metric. |
Look Past The Threshold
Thresholds are tools for planning and control, not a verdict. A single cut-off can hide both weak evidence near the line and strong evidence far from it. Two studies can share the same label yet point to very different decisions. Read the full pattern: size, direction, precision, and risks.
Start With Effect Size And Precision
Measures such as risk difference, risk ratio, odds ratio, mean change, or hazard ratio answer different questions. Choose the one that matches the decision you care about. Then study the interval. Wide intervals warn that the estimate is unstable. Tight intervals show a narrow set of compatible effects. Graphs with CIs across outcomes help readers judge patterns at a glance.
Weigh Clinical Relevance
Ask whether the estimate clears a clinical bar such as a minimal clinically important difference. A tiny shift may pass a statistical test yet leave patients unchanged. A moderate shift that matters to patients may miss a threshold in an underpowered study; that is a sign to gather better data, not a cue to chase post hoc analyses.
Respect Study Design And Bias Risks
Randomization, allocation concealment, blinding, follow-up, and outcome adjudication shape how much weight you can place on a result. Check for imbalances, protocol deviations, and selective reporting. Call out early stopping rules and any data-driven changes to endpoints or analysis sets.
Guard Against Multiplicity, Flexibility, And Bias
Modern trials track many outcomes, time points, and subgroups. Each extra look raises the chance of a spurious win. Flexibility in modeling can tilt results, too. A sound paper shows how the team controlled these risks and how conclusions change under alternate choices.
Control Multiplicity
Spell out how the analysis handled co-primary or multiple secondary endpoints. Options include gatekeeping, alpha splitting, and corrections such as Holm, Hochberg, or false discovery rate control. Regulators also expect a clear plan for familywise error in confirmatory settings; readers should see that plan before looking at results.
Stick To The Protocol
Point to a registry or protocol and flag any deviations. Mark analyses as primary, major secondary, or exploratory. Separate adjusted from unadjusted runs. Note any interim looks and the stopping boundary used. Transparency keeps credibility intact.
Show Sensitivity
Run analyses that test the stability of findings: alternate covariate sets, different missing-data methods, or outcome definitions. Report how these choices affect estimates and intervals. Place numbers in a figure or a compact table so readers can scan the pattern.
Major groups have published guidance that matches this approach. The American Statistical Association’s statement on p-values lays out six principles on use and interpretation. For trial reports, the CONSORT 2025 statement emphasizes clear effect estimates, confidence intervals, prespecified outcomes, and transparent analysis plans.
When A Result Fails The Threshold
Do not stop reading. Ask whether the interval still allows a benefit that matters to patients. If so, precision is the issue. If the interval rules out any worthwhile gain, the study informs practice even without a pass at alpha. Explain which of those two cases applies, and say what new data would change the call.
In confirmatory trials, a miss at the primary endpoint closes the door on formal claims from that trial. Post hoc stories can mislead. Treat secondary outcomes and subgroups as hypotheses to test in later work unless the plan built a gatekeeping path for them.
Better Ways To Tell The Story
Confidence Intervals And Plain Graphics
Show the estimate and its CI for each outcome on a single scale. Add a vertical line for the null value and a band for the minimal clinically important difference. Readers can judge direction and precision in one view.
Bayesian Summaries Where Suitable
When a prior and a loss function are well justified, Bayesian models can express the chance that an effect exceeds a clinical bar. Report the model, the prior, the posterior interval, and a set of sensitivity runs. Keep claims anchored to decisions, not to a fixed cut-off.
Risk, Benefit, And Cost
Pair statistical findings with safety, quality of life, and resource use. A modest gain may make sense only if risks stay low and costs are sustainable. A clear table or figure that layers benefit and harm helps readers reach a decision that fits patients and settings.
Manuscript Review Checklist
Use this compact list when you review a draft or revise your own paper.
| Item | What To Look For | Red Flags |
|---|---|---|
| Question & Endpoint | Clear primary question; outcome aligns with care. | Vague aims; switched endpoints. |
| Effect Size | Right measure (risk difference, ratio, hazard) and scale. | Only p-values; no magnitude. |
| Precision | Intervals reported and interpreted. | No CI; over-emphasis on thresholds. |
| Clinical Relevance | Link to a clinical threshold such as MCID. | Claims with no tie to patient value. |
| Multiplicity | Prespecified plan for multiple endpoints or looks. | Unplanned cherry-picking. |
| Protocol & Registry | Public plan; deviations explained. | Unregistered or opaque changes. |
| Missing Data | Methods described; sensitivity runs shown. | Complete-case only without justification. |
| Subgroups | Few, prespecified, with interaction tests. | Many post hoc slices with bold claims. |
| Harms | Adverse events summarized with the same care as benefits. | Benefits only; safety hidden in appendices. |
| Language | Neutral wording; exact numbers; no asterisks. | “Safe and effective” style claims from a single test. |
Reporting Language That Keeps Readers Safe
Careful wording helps readers draw the right lesson. Swap bright-line labels for clear statements. Try lines such as “the estimated risk ratio was 0.82 (95% CI 0.70 to 0.96), which clears the clinical bar of 0.90 set in the protocol,” or “the 95% CI spans both small benefit and no effect; a larger, longer trial is needed.” Avoid phrases that overclaim certainty.
Bottom Line For Researchers And Reviewers
Statistical significance is a gatekeeper, not the star. Lead with the estimate, its precision, and its meaning for patients. Keep the analysis tied to a plan. Show how choices and assumptions move the numbers. Use clear language that respects uncertainty. Do that, and your work will guide decisions with care and clarity.