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Type II Error
The error made when a false null hypothesis is not rejected, missing the detection of a real effect.
Type I Error
The mistake of rejecting the null hypothesis when it is actually true.
Null Hypothesis
The default hypothesis that there is no effect or no difference, and any observed effect is due to sampling variability.
Type I Error
The probability of rejecting the null hypothesis when it is actually true, also known as a false positive.
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