By Learn Statistics Easily
Normal distributions underpin many statistical tests; validating this assumption is crucial for accurate analysis.
Razali and Wah's research highlights the relative power of four key normality tests under various conditions.
The test compares observed data against a normal distribution, with a low W indicating significant deviation.
While versatile, the Kolmogorov-Smirnov test may overlook deviations in the distribution tails.
This test adjusts the K-S approach for small samples by estimating key population parameters.
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Anderson-Darling gives extra weight to tail data, making it adept at spotting deviations from normality.
Selecting the right normality test can profoundly affect the validity of your statistical conclusions.
The effectiveness of normality tests is closely tied to the size of the sample being analyzed.
Choosing the correct normality test hinges on understanding your dataset's characteristics and needs.
Among normality tests, Shapiro-Wilk stands out for its high power in small to moderate samples.
For a comprehensive understanding of normality tests, refer to the detailed article.