Variable Significance Testing
Updated 2026-09-11
INTRODUCTION
English translation pending.
CORE DEFINITION
Variable significance testing is the standard inferential procedure of frequentist statistics, using tests such as the t-test, F-test, and chi-square, and it is reported through p-values and confidence intervals. Its core proposition is that under the null hypothesis of no effect, the observed statistic has a known distribution, so a result far into the tail is unlikely to be pure chance and the null can be rejected. The key qualification is that significance is not importance: a small but precisely estimated effect can be significant, and the p-value does not measure the size or the practical value of the effect.
SCAFFOLDING EFFECT
Reduce cognitive load
- Use hypothesis setup: state the null of no effect and choose a significance level. - Use test selection: pick t, F, or chi-square according to the data type. - Use effect reporting: give the effect size and confidence interval alongside the p-value.
Anchor fast decisions
Random sampling produces variation even when nothing is happening, so an observed difference is never self-evidently real. Significance testing supplies a reference: it asks how often such a difference would appear if the variable had no effect. A result in the tail of that reference distribution is hard to explain by chance, which licenses rejecting the null. The logic is asymmetric, since failure to reject only means the evidence was insufficient, and it is easy to abuse, because running many tests until one crosses the threshold manufactures significance from noise.
MINIMUM ACTION
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Source support: Explicit
- zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E6%98%BE%E8%91%97%E6%80%A7%E5%B7%AE%E5%BC%82verified
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