Introduction
Non-parametric tests are statistical methods that do not rely on assumptions about the distribution of data. Unlike parametric tests, which often require normality and homogeneity of variance, non-parametric tests can be applied to data that are ordinal, skewed, or have small sample sizes. These tests are particularly valuable in real-world research where strict statistical assumptions cannot always be met.
When to Use Non-Parametric Tests
Non-parametric tests should be used when:
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The data are not normally distributed.
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The sample size is very small.
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The data are ordinal or ranked rather than measured on an interval/ratio scale.
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Outliers or extreme values make parametric tests unreliable.
Strengths of Non-Parametric Tests
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They are flexible and can be applied to a wide variety of data types.
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They require fewer assumptions about the population.
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Robust against outliers and skewed distributions.
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Suitable for small sample sizes.
Limitations of Non-Parametric Tests
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Less powerful than parametric tests when assumptions of parametric tests are satisfied.
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Provide less detailed information about parameters (e.g., mean and variance).
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May require larger samples to achieve the same statistical confidence.
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Often limited to hypothesis testing, not parameter estimation.
Conclusion
Non-parametric tests are essential tools in statistics, offering flexibility and robustness when data do not meet parametric assumptions. While they have certain limitations, they remain a critical choice for researchers dealing with non-normal, ordinal, or small datasets.
