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Everything about Normality test
Class of statistical tests
In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Normality testA 5-minute orientation
- Skim a recent paper: Comparison of Normality Tests in Terms of Sample Sizes under Different Skewness and Kurtosis CoefficientsInfluential work since 2022
- Pick up a classic book: Distributions of Test StatisticsThe most-reprinted book on the subject
- Read the landmark paper: An analysis of variance test for normality (complete samples)Cited 15,424 times
- Go sideways: D'Agostino's K-squared testA closely related topic
- Go sideways: Shapiro–Wilk testA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
An analysis of variance test for normality (complete samples)
The “Reading the Mind in the Eyes” Test Revised Version: A Study with Normal Adults, and Adults with Asperger Syndrome or High‐functioning Autism
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis.
An Analysis of Variance Test for Normality (Complete Samples)
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown
Efficient tests for normality, homoscedasticity and serial independence of regression residuals
🆕 Influential since 2022
Comparison of Normality Tests in Terms of Sample Sizes under Different Skewness and Kurtosis Coefficients
Shapiro-Wilk test for multivariate skew-normality
A Structured Guide to Univariate Test Selection Based on Normality, Variance Homogeneity, and Graphical Data Exploration
A normality test for multivariate dependent samples
A robust alternative to the Lilliefors test of normality
📚 Books worth reading
Distributions of Test StatisticsA. M. Mathai · 1990
Probabilités et statistique appliquéesB. Lacaze · 1997
Testing for normalityHenry C. Thode · 2002
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- D'Agostino's K-squared test In statistics, D'Agostino's K2 test, named for Ralph D'Agostino, is a goodness-of-fit measure of departure from normality, that is the test aims to gauge…
- Shapiro–Wilk test The Shapiro–Wilk test is a test of normality. It was published in 1965 by Samuel Sanford Shapiro and Martin Wilk. The Shapiro–Wilk test tests the null…
- Lilliefors test Lilliefors test is a normality test based on the Kolmogorov–Smirnov test. It is used to test the null hypothesis that data come from a normally distributed…
- Jarque–Bera test In statistics, the Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution. The…
- Shapiro–Francia test The Shapiro–Francia test is a statistical test for the normality of a population, based on sample data. It was introduced by S. S. Shapiro and R. S. Francia…
- Normal probability plot The normal probability plot is a graphical technique to identify substantive departures from normality. This includes identifying outliers, skewness, kurtosis…
- Student's t-distribution In probability theory and statistics, Student's t distribution (or simply the t distribution) tν is a continuous probability distribution that generalizes…
- Q–Q plot In statistics, a Q–Q plot (quantile–quantile plot) is a probability plot, a graphical method for comparing two probability distributions by plotting their…