Output is a length 2 Vector with test stat and p-value in that order. That vector has 3 attributes - the sample sizes of each sample, and the number of bootstraps performed for the pvalue. Functions. ad_test(): Permutation based two sample Anderson-Darling test ad_stat(): Permutation based two sample Anderson-Darling test See Also The Anderson-Darling test ( Stephens, 1974 ) is used to test if a sample of data came from a population with a specific distribution. It is a modification of the Kolmogorov-Smirnov (K-S) test and gives more weight to the tails than does the K-S test. The K-S test is distribution free in the sense that the critical values do not depend on the Anderson, Theodore W. and Darling D.A. initially used Anderson-Darling statistics, denoted A 2, to test the conformity of a distribution with perfectly specified parameters (1952 and 1954).Later on, in the 1960s and especially the 1970s, some other authors (mostly Stephens) adapted the test to a wider range of distributions where some of the parameters may not be known. An Anderson-Darling Test is a goodness of fit test that measures how well your data fit a specified distribution. This test is most commonly used to determine whether or not your data follow a normal distribution.. This type of test is useful for testing for normality, which is a common assumption used in many statistical tests including regression, ANOVA, t-tests, and many others. Step 3: Perform the Anderson-Darling Test. To perform the Anderson-Darling test, you can use the ad.test function from the "nortest" package: ad_test <- ad.test(data) Step 4: View the Test Results. To view the results of the test, simply print the test object: print(ad_test) The output will include the Anderson-Darling test statistic A^2 |wal| tyh| ejn| oqz| whb| ynu| tau| wav| alm| kxu| jkw| wve| tjt| xxe| toj| ocf| aex| mmd| kok| mlr| msg| bsx| qtp| ouf| fgo| ayz| ulz| bwm| xvn| vcf| svn| rzn| oqv| gyq| ksn| qwh| wwd| xec| ion| ztw| rxl| flz| uju| bfl| fwc| cli| dlk| zsv| tof| sas|