Direct answer
Statistical significance concerns a result’s compatibility with a specified null model and procedure; practical importance concerns whether the estimated effect is large, precise, relevant, and consequential enough to matter.
Visual explanation
Detectable is not automatically important
What this calculation tells you
Significance testing compresses one model comparison into a tail probability and threshold rule. Practical decisions need the observed effect, confidence interval, measurement scale, baseline risk, costs, benefits, and study quality.
Define a meaningful effect before looking at results. Report estimates and intervals, then discuss whether values across that range would change a decision.
Where it is used
Research
Lead with magnitude and uncertainty rather than threshold labels.
Product analysis
Compare measured changes with predeclared decision relevance.
Quality
Separate detectable shifts from operational tolerances.
Public communication
Avoid binary claims that exceed the evidence.
Common situations
- Reading a statistically significant tiny effect.
- Interpreting an inconclusive interval.
- Defining a minimum relevant effect.
- Comparing relative and absolute differences.
Start with the statistical question
Define a meaningful effect before looking at results. Report estimates and intervals, then discuss whether values across that range would change a decision.
Two studies show the same tiny effect: a large sample yields a small p-value while a small sample does not. A second pair shows the same p-value with different effect scales.
Worked example
A difference of 0.1 units can be statistically significant with an enormous n yet irrelevant to users. A clinically or operationally meaningful difference can remain uncertain in a small study without becoming zero.
Assumptions that carry the result
Interpretation depends on valid design, measurement, model, analysis plan, and transparency about multiplicity and selection. A threshold does not repair any of these.
Interpret the result without overreaching
Neither significance nor an effect estimate alone proves causation, replication, generalizability, safety, or a recommended action.
- Equating p<0.05 with an important result.
- Equating p≥0.05 with no difference.
- Reporting relative effects without absolute scale or baseline context.
Practical questions
Frequently asked questions
Does p<0.05 mean the result matters?
No. It does not measure effect magnitude, benefit, harm, or decision relevance.
Does non-significant mean equal?
No. It may reflect limited precision or an interval containing both meaningful and negligible effects.
What should be reported with a p-value?
At minimum the effect estimate, uncertainty interval, sample/design context, method, and relevant limitations.
Further reading
Authoritative sources
Use these primary and professional resources to check definitions, conventions, or requirements that may extend beyond this guide.