During a statistical test, which metric indicates how likely it is to see a result as unusual as the one observed, given the baseline assumption is true?
p-values
Bayes factors
confidence intervals
effect sizes
This metric is crucial for hypothesis testing. 'p-values' denote the probability of obtaining a result as extreme as what was observed, assuming the null hypothesis is true. Other incorrect choices include metrics like 'confidence intervals' for estimating ranges rather than probabilities, 'effect sizes' for measuring magnitude rather than likelihood, and 'Bayes factors' for evaluating model comparisons.
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