Non parametric
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Non-parametric tests make fewer assumptions about the distribution of data compared to parametric tests. They can be used as alternatives when the assumptions of parametric tests like the t-test, ANOVA, and other tests are violated. Some common non-parametric tests include the Mann-Whitney U test and Wilcoxon signed-rank test for comparing two independent and paired samples respectively, and the Kruskal-Wallis H test for comparing more than two independent samples. These tests convert original data to ranks before performing the statistical analysis to avoid distributional assumptions.
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Here are the steps to solve this problem:
1. State the hypotheses:
H0: 亮 = 100
H1: 亮 100
2. The critical values are 賊1.96 (two-tailed test, 留=0.05)
3. Compute the test statistic:
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5. There is strong evidence that the medication affected intelligence since the sample mean is much higher than the population mean.Ad
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