In hypothesis testing, the p-value represents the probability of observing a test statistic at least as extreme as the one observed, assuming the null hypothesis is true.
Key idea:
A low p-value (typically below a chosen significance level such as 0.05):
Indicates that the observed result is unlikely under the null hypothesis.
Suggests there is evidence against the null hypothesis.
Is therefore interpreted as potentially meaningful / statistically significant.
By contrast:
The test statistics themselves (e.g., t-statistic, F-statistic, chi-square) are usually interpreted such that larger absolute values indicate stronger evidence against the null (and lead to smaller p-values).
The question explicitly asks which test statistic shows that a low value implies meaningful result — that is p-value, which is itself a probability metric used to evaluate significance.
Study materials: explanation that small p-values indicate statistically significant results.
Contribute your Thoughts:
Chosen Answer:
This is a voting comment (?). You can switch to a simple comment. It is better to Upvote an existing comment if you don't have anything to add.
Submit