For a significance level, α, chosen before you conduct your test, a p-value (P) less than α indicates that the data do not follow that distribution.
Minitab performs goodness-of-fit tests on your data for a variety of distributions and estimates their parameters. Choose the distribution that best fits your data, and is most appropriate for your analysis. If more than one distribution fits your data, select the distribution with the largest p-value.
For every 3-parameter distribution except the Weibull distribution, there is no established method for calculating the p-value, so you must use the likelihood-ratio test (LRT).
Also, a visual inspection of the probability plot combined with the Anderson-Darling value can help indicate whether the distribution is a good fit. However, it may be better to choose a distribution which has a calculated p-value and a similar Anderson-Darling value.
For some distributions a closed form expression for the p-value exists and thus an exact p-value can be obtained. However, for certain other distributions a closed form expression does not exist but tables of ranges of p-values, obtained through simulation studies, are available. For these distributions Minitab can only report a lower and/or upper bound for the p-value.
An asterisk is displayed instead of a p-value for the 3-parameter lognormal, 3-parameter gamma, and 3-parameter loglogistic distributions. The asterisk indicates that Minitab cannot calculate a p-value for that distribution.