MATH 10041 Lecture Notes - Lecture 25: Null Hypothesis, Test Statistic, Statistical Hypothesis Testing
30 Mar 2019
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Identify two possible sources of error in hypothesis testing: distinguish between practical and statistical significance. Identify similarities and differences between a hypothesis test and confidence interval approach. Revisiting small p-values: a small p-value means our test statistic is extreme, an extreme test statistic means something unusual, and therefore unexpected, has happened, small p-values lead us to reject the null hypothesis. If the conditions concerning the sampling distribution of the z-statistic fail to be met, then we cannot find a p-value using the normal curve. Conditions can fail for the following reasons: the sample size is too small, samples are not randomly selected in this case conclusions may not generalize to the population. Example: describing mistakes: suppose a political analyst is interested in predicting whether a school bond measure on the ballot will pass. Her hypotheses are: h0: p = 0. 50, ha: p > 0. 50, describe two types of errors she might make in conducting this test.
