In hypothesis testing, what does a p-value represent?
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A p-value represents the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.
In hypothesis testing, a p-value represents the probability of obtaining test results at least as extreme as the observed results, assuming that the null hypothesis is true. A low p-value indicates strong evidence against the null hypothesis, leading researchers to consider rejecting it in favor of the alternative hypothesis.
In hypothesis testing, a p-value represents the probability of obtaining results at least as extreme as the observed data, assuming the null hypothesis is true; it helps determine whether to reject the null hypothesis based on a predetermined significance level.
In hypothesis testing, a p-value represents the probability of obtaining results at least as extreme as the observed data, assuming the null hypothesis is true; it helps determine whether to reject the null hypothesis based on a predetermined significance level.