Lessons
- What is Hypothesis Testing
- Test Statistic, Type I and type II Errors, and Significance Level
- Decision Rule in Hypothesis Testing
- p-Value in Hypothesis Testing
- Selecting the Appropriate Test Statistic
- Hypothesis Testing with t-statistic
- Hypothesis Testing with z-statistic
- Tests Concerning Differences in Means
- Paired Comparision Tests - Mean Differences When Populations are Not Independent
- Hypothesis Tests Concerning Variances
- Chi-square Test – Test for value of a single population variance
- F-test - Test for the Differences Between Two Population Variances
- Non-parametric Tests
Non-parametric Tests
In parametric tests we make assumptions about the distribution of the population and each parametric test is specific to a population parameter such as mean or variance, for example, a z-test.
A nonparametric test is not concerned with a parameter or makes minimal assumptions about the population being sampled.
A nonparametric test is primarily used in three situations:
- when data does not meet distributional assumptions
- when data are given in ranks
- when the hypothesis we are addressing does not concern a parameter
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