Hypothesis testing
Every term in the four-step test, including the two errors and the ones students most often state backwards.
10 terms
Alternative hypothesisThe alternative hypothesis, written Ha, is the claim of a real effect or difference that you gather evidence for by ruling out the null hypothesis.Chi-square testA chi-square test compares observed counts of categorical data to the counts expected under a hypothesis, gauging how far the data stray from that model.Expected countAn expected count is how many observations a category would get if the null hypothesis were exactly true; it is the baseline in a chi-square test.Hypothesis testA hypothesis test uses sample data to weigh a null claim against an alternative, gauging how surprising the data would be if the null claim were true.Null hypothesisThe null hypothesis, written H0, is the default claim of no effect or no difference, assumed true unless the sample data give strong evidence against it.P-valueA p-value is the probability, computed assuming the null hypothesis is true, of getting a result at least as extreme as the one you observed.PowerThe power of a test is the probability it correctly rejects a false null hypothesis, equal to 1 minus the Type II error rate.Significance levelThe significance level, alpha, is the threshold a p-value is compared to, set before testing as the accepted probability of rejecting a true null hypothesis.Type I errorA Type I error is rejecting a true null hypothesis: a false positive, concluding there is an effect when in fact there is none.Type II errorA Type II error is failing to reject a false null hypothesis: a false negative, missing a real effect that is actually present.