Scenario
You conducted an improvement experiment, increasing the screw torque from 5Nm to 5.5Nm. After running statistical analysis, the P-value was 0.043. Your supervisor asked, "Is it effective?" You replied, "Yes, P < 0.05."
The supervisor was satisfied, and you wrote the new parameter into the SOP. Three months later, the same problem reappeared. You investigated and found that the sample size for that instance was only 12.
Plain Language Explanation
The P-value is not the answer to "Is this improvement effective?" It only answers one question:
If this improvement has "absolutely no effect," what is the probability of randomly obtaining such a favorable result?
P = 0.043 means: Assuming your improvement is ineffective, the probability of randomly drawing this specific set of data is 4.3%. Because it's very low, we say it's "significant."
However, "significant" does not equal "important." A result with P = 0.001 might only show an effect of 0.01mm—statistically highly significant, but practically irrelevant from an engineering perspective.
The P-value should be considered together with "Effect Size."
Practical Judgment
| P-value | Interpretation | Action |
|---|---|---|
| > 0.05 | Difference may be random fluctuation | Do not easily adjust the process |
| 0.01–0.05 | Statistically significant | Check if the effect size makes it worth changing |
| < 0.01 | Strongly significant | Can confidently say there is an effect |
| Sample size < 30 | P-value unreliable | Increase sample size first |
3 scenarios where you cannot solely rely on P-value:
- Very large sample size (n > 500): Almost any minor difference will become significant.
- Multiple comparisons (running many groups simultaneously): For each additional group run, the probability of false positives accumulates.
- Data does not conform to normality: The P-value of a t-test may be inaccurate.
How InsightFab Does It
Minitab and JMP both only provide you with a P-value, requiring you to determine the effect size yourself. In addition to the P-value, InsightFab directly gives you confidence intervals and an effect size interpretation, telling you, "statistically significant, but the effect size is small; please evaluate whether it's practically worth improving."
Golden Quote
"A P-value below 0.05 is your talisman, but it only protects you from being statistically disproven—it does not protect you from making correct engineering decisions."