non parametric tests
Overview
This lesson introduces non-parametric tests, a class of statistical methods used when assumptions about the population distribution (e.g., normality) cannot be met or when dealing with ordinal data. We will explore their advantages and disadvantages, and delve into specific tests like the Sign Test, Wilcoxon Signed-Rank Test, and Mann-Whitney U Test.
Introduction to Non-parametric Tests
Non-parametric tests are statistical methods that do not require the data to follow a specific distribution, such as the normal distribution. This makes them highly valuable when dealing with data that is **skewed**, has **outliers**, or is measured on an **ordinal scale**. Unlike parametric tests (...
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Key Concepts
- Non-parametric Tests: Statistical tests that do not rely on assumptions about the population distribution, particularly useful for non-normal data or ordinal scales.
- Parametric Tests: Statistical tests that assume data comes from a specific distribution (e.g., normal distribution) and often require interval or ratio data.
- Sign Test: A non-parametric test used for paired data to determine if there is a consistent difference between two conditions, based only on the direction of the difference.
- Wilcoxon Signed-Rank Test: A non-parametric test for paired data that considers both the direction and magnitude of differences, providing more power than the Sign Test.
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Exam Tips
- →Clearly identify whether data is paired or independent, as this dictates the appropriate test (Sign/Wilcoxon for paired, Mann-Whitney for independent).
- →Remember the steps for ranking data, especially handling ties by assigning average ranks. This is a common source of error.
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