How to classify hypothesis testing？
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How to classify hypothesis testing？
Hypothesis testing can be classified into two broad categories:
1. Parametric hypothesis testing: This type of hypothesis testing assumes that the data follows a specific distribution, such as the normal distribution. It involves testing hypotheses about the population parameters, such as the mean and variance. Examples of parametric hypothesis tests include t-tests, ANOVA, and chi-square tests.
2. Nonparametric hypothesis testing: This type of hypothesis testing does not assume any specific distribution for the data. It involves testing hypotheses about the population using statistical methods that do not rely on the assumptions of parametric tests. Examples of nonparametric hypothesis tests include Wilcoxon rank-sum test, Kruskal-Wallis test, and Mann-Whitney U test.
1. Parametric hypothesis testing: This type of hypothesis testing assumes that the data follows a specific distribution, such as the normal distribution. It involves testing hypotheses about the population parameters, such as the mean and variance. Examples of parametric hypothesis tests include t-tests, ANOVA, and chi-square tests.
2. Nonparametric hypothesis testing: This type of hypothesis testing does not assume any specific distribution for the data. It involves testing hypotheses about the population using statistical methods that do not rely on the assumptions of parametric tests. Examples of nonparametric hypothesis tests include Wilcoxon rank-sum test, Kruskal-Wallis test, and Mann-Whitney U test.
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