adtest
              Anderson-Darling goodness-of-fit hypothesis test.
 h = adtest (x) returns a test decision for the null
 hypothesis that the data in vector x is from a population with a normal
 distribution, using the Anderson-Darling test.  The alternative hypothesis is
 that x is not from a population with a normal distribution. The result
 h is 1 if the test rejects the null hypothesis at the 5% significance
 level, or 0 otherwise.
 h = adtest (x, Name, Value) returns a test
 decision for the Anderson-Darling test with additional options specified by
 one or more Name-Value pair arguments.  For example, you can specify a null
 distribution other than normal, or select an alternative method for
 calculating the p-value, such as a Monte Carlo simulation.
The following parameters can be parsed as Name-Value pair arguments.
| Name | Description | 
|---|---|
| "Distribution" | The distribution being tested for. It tests whether x could have come from the specified distribution. There are two choise available for parsing distribution parameters: | 
| "Alpha" | Significance level alpha for the test. Any scalar numeric value between 0 and 1. The default is 0.05 corresponding to the 5% significance level. | 
| "MCTol" | Monte-Carlo standard error for the p-value, pval, value. which must be a positive scalar value. In this case, an approximation for the p-value is computed directly, using Monte-Carlo simulations. | 
| "Asymptotic" | Method for calculating the p-value of the Anderson-Darling test, which can be either true or false logical value. If you specify ’true’, adtest estimates the p-value using the limiting distribution of the Anderson-Darling test statistic. If you specify ’false’, adtest calculates the p-value based on an analytical formula. For sample sizes greater than 120, the limiting distribution estimate is likely to be more accurate than the small sample size approximation method. | 
 [h, pval] = adtest (…) also returns the p-value,
 pval, of the Anderson-Darling test, using any of the input arguments
 from the previous syntaxes.
 [h, pval, adstat, cv] = adtest (…) also
 returns the test statistic, adstat, and the critical value, cv,
 for the Anderson-Darling test.
The Anderson-Darling test statistic belongs to the family of Quadratic Empirical Distribution Function statistics, which are based on the weighted sum of the difference over the ordered sample values , where is the hypothesized continuous distribution and is the empirical CDF based on the data sample with sample points.
See also: kstest
Source Code: adtest