1001Ferramentas
🔔 Calculators

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).

Resultado

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).

Two symptoms of non-normality in one test

There are many ways a distribution can depart from the normal curve, but two are the most common: leaning to one side, skewness, and having tails heavier or lighter than normal, kurtosis. The D'Agostino-Pearson test examines both at once and combines them into a single verdict, the so-called omnibus normality test.

The method transforms the sample skewness and the sample kurtosis into standardized z scores, each measuring how far that aspect strays from what's expected under normality. Summing the squares of those two scores gives the K² statistic, which follows a chi-square distribution with two degrees of freedom. A large K² indicates that at least one of the two aspects, or both, depart from normal.

Enter the sample, with at least eight values, since the transformations need a minimum of data to work well. The tool returns the K² statistic and the p-value. It's one of the most used normality tests in statistical practice, precisely because it gathers skewness and kurtosis into a single interpretable number.

Related Tools

🔗

Correlation Significance Test

Computes the significance test for the Pearson correlation coefficient, answering whether the observed correlation between two variables is real or could have arisen by chance. A correlation of 0.5 in a sample of 6 points is unimpressive; the same correlation in 600 points is very strong. The test converts r into a t value, with n−2 degrees of freedom, and returns the p-value. The tool computes r, the t statistic and the two-sided p-value. Enter the two series of paired 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.

🔔

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.

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.