Statistical Significance
Updated 2026-08-08
INTRODUCTION
English translation pending.
CORE DEFINITION
A result is statistically significant when the probability of observing data at least as extreme, assuming the null hypothesis is true, falls below a pre-set threshold such as 0.05. Significance addresses only whether chance is a plausible explanation; it says nothing about the size of the effect, its practical importance, its replicability or whether the relationship is causal. Effect size, robustness across methods and consistency with other studies carry that information.
SCAFFOLDING EFFECT
Reduce cognitive load
- Read it as a gate: treat significance as a minimum bar rather than as the conclusion. - Check the size: ask how large the effect is and whether it matters in practice. - Test robustness: re-run under different specifications to see whether the effect survives.
Anchor fast decisions
The p-value measures the compatibility of the data with a null hypothesis, so a small value means chance alone is an unlikely explanation. It is a function of both the effect size and the sample size, so a trivial effect becomes significant in a large sample. The measure therefore answers a narrow question about chance rather than a broad one about importance.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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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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