Anderson-Darling Test (Normality)
Computes the Anderson-Darling test, one of the most powerful normality tests available. It measures how far the empirical distribution of your data departs from the normal curve, giving extra weight to the tails — precisely where other tests often miss departures. The tool returns the A² statistic, the adjusted A²* version (corrected for sample size) and an approximate p-value. The smaller the A², the more compatible with normality. Enter at least 8 values.
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Anderson-Darling Test (Normality)
Computes the Anderson-Darling test, one of the most powerful normality tests available. It measures how far the empirical distribution of your data departs from the normal curve, giving extra weight to the tails — precisely where other tests often miss departures. The tool returns the A² statistic, the adjusted A²* version (corrected for sample size) and an approximate p-value. The smaller the A², the more compatible with normality. Enter at least 8 values.
Do your data really follow a normal distribution?
A good share of statistical methods assume normality, and checking that assumption before trusting the results is hygiene that saves a lot of headaches. Among the various normality tests, Anderson-Darling stands out for a specific virtue: it is especially attentive to the tails of the distribution, which is where the fit to a normal tends to fail first.
The test standardizes the data, sorts them, and compares the observed cumulative distribution with that of the theoretical normal, weighting the discrepancies so as to amplify what happens at the extremes. The result is the A² statistic, which grows as the data depart from normality. Because small samples distort the statistic a bit, a correction is applied to produce the adjusted A²* version, from which the p-value follows.
Enter at least eight values. The tool returns A², the adjusted A²* and an approximate p-value via Stephens' classic formula. A small A² and a high p-value indicate no reason to reject normality; a large A² suggests that another distribution model, or a transformation of the data, may be more appropriate.
Related Tools
D'Agostino-Pearson Normality Test (K²)
Computes the D'Agostino-Pearson omnibus normality test, which combines two clues of non-normality into a single verdict: skewness (do the data lean to one side?) and kurtosis (are the tails too heavy or too light?). Each becomes a standardized z score, and the sum of their squares, the K² statistic, follows a chi-square with 2 degrees of freedom. It's more informative than looking at skewness and kurtosis separately. Enter the sample (at least 8 values).
Kolmogorov-Smirnov Test (vs Normal)
Computes the one-sample Kolmogorov-Smirnov test against a normal distribution with specified mean and standard deviation. It measures the largest vertical distance between your data's empirical cumulative distribution and the theoretical normal curve — the D statistic. Unlike tests that look only at skewness or kurtosis, the KS compares the entire distributions, point by point. Enter the sample and the normal parameters (mean and standard deviation) to test.
Grubbs' Test (Outlier)
Computes Grubbs' test, used to identify whether the most extreme value in a sample is a statistical outlier, under the assumption that the data would be normal without it. It compares the deviation of the farthest point from the mean, in standard-deviation units, against a critical value derived from the t distribution. If the G statistic exceeds the critical value, the point is flagged as an outlier. It's the classic test for cleaning data before an analysis. Enter the data and the confidence level.
The results provided by this tool are for general informational and educational purposes only and do not constitute professional, financial, medical, legal, tax or accounting advice. Always confirm important decisions with a qualified professional and official sources.