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U-M STATS 250 - Sample Size, Statistical Significance, and Importance
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STATS 250 1st Edition Lecture 10 Outline of Last Lecture I. HT Module 0: an Overview of Hypothesis TestingII. HT Module 1: Testing Hypotheses About a Population ProportionIII. If n is SmallOutline of Current Lecture I. Sample Size, Statistical Significance, and Practical ImportanceCurrent LectureI. Sample Size, Statistical Significance, and Practical Importancea. When there is a small to moderate effect in the population, a small sample has little chance of providing statistically significant support for the alternative hypothesisb. With a large sample, even a small and unimportant effect in the population may lead to a conclusion of statistical significancec. “Statistically Significant” only means that the data is strong enough to reject the null hypothesisd. Warning 1: small samples make it very difficult to demonstrate much of anythinge. Warning 2: a huge sample size can make a practically unimportant difference statistically significantf. Types of Errorsi. Type 1 error = rejecting H0 when H0 is trueii. Type 2 error = failing to reject H0 when Ha is true1. Probability of making a type 2 error = β2. Probability of rejecting H0 when Ha is true = 1 – β (called the power of the


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U-M STATS 250 - Sample Size, Statistical Significance, and Importance

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