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Descriptive Statistics
Normality Check
Screen distribution shape using skewness, kurtosis and Jarque-Bera approximation.
Calculation engine
Client-side JavaScript; no external statistics package is loaded.
Results are calculated in the browser and are not uploaded.
Client-side JavaScript; no external statistics package is loaded.
Results are calculated in the browser and are not uploaded.
Method overview
When to use
Use as a quick assumption check before t-tests, ANOVA or linear models. It should be combined with visual inspection when possible.
Input requirements
Enter one numeric vector. At least 3 values are required.
Key assumptions
- A numeric sample is required.
- This is an approximate screening test, not a complete diagnostic.
- For small samples, visual assessment and subject-matter judgment are important.
Null hypothesis
H₀: the data are compatible with a normal distribution.
Method used
Jarque-Bera statistic based on skewness and excess kurtosis with chi-square approximation.
References
- Jarque CM, Bera AK. Efficient tests for normality, homoscedasticity and serial independence. Economics Letters, 1980.
Data input and results
Enter one numeric sample. This quick check summarizes skewness, excess kurtosis and the Jarque-Bera approximation.
No calculation has been performed.