I know this is a stale question, but John's answer vis-a-vi Henry Ford is a bit misleading. You can use Anderson-Darling to test for ANY continuous distribution. The adtest function allows you to specify different distributions by name or with a distribution object. Note that if you create a distribution object, you must specify the parameters of the distribution, which might be unknown. The problem is the critical values of the test statistic can be slightly different if you use estimates of the parameters for that particular distribution (e.g. sample mean and variance) instead of true values. The critical values are also slightly different depending on the number of samples in your data.
In general, if you know the CDF function and can generate random data that follows a specific distribution, then you can generate thousands of cases, calculate the AD test statistic for each case, histogram the ad values, and determine the critical values for various levels of significance yourself. If you really want to understand Anderson-Darling, you should go through this exercise. The adtest function does this monte carlo process for you when you pass in a distrbution object. For the built-in supported distributions (norm, exp, ev, logn, weibull) it probably uses precomputed critical value tables and adjusts for the number of samples. These published tables were originally generated by monte carlo analysis back in the mainframe days, and some of the published tables have been found to have errors. I believe the table values assume the distribution parameters are unknown. YMMV.
Note that the AD test is sensitive because it pays more attention to the tails of the distribution, since the tails are where distributions differ the most. This makes the AD test very sensitive to outliers. You will be tempted to start removing outliers from your data, but be careful. If you remove enough outliers, all distributions end up looking uniform!
