Statistical significance is the probability that the observed results, or more extreme ones, would occur if the null hypothesis were true. The rejection simply confirms that an effect exists in the data, not that it is meaningful in a substantive or clinical sense.
Understanding Statistical Significance in Decision Making
However, this binary label—"significant" versus "not significant"—does not measure the size or importance of the effect. It suggests that the observed data is inconsistent with the assumption of "no effect.
These tools transform a rigid yes/no decision into a rich description of the data, ensuring that the finding of significance is both meaningful and interpretable. The Role of Effect Size and Confidence Moving beyond the simple binary of significant or not requires looking at additional metrics that provide context.
Understanding Statistical Significance in Decision Making
The Null Hypothesis and the Threshold of Significance To grasp the conclusion, one must first understand the premise. Interpreting the Decision Correctly Accepting that rejection implies significance, it is vital to understand what this label actually communicates.
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