KStest(distribution, data, significance=0.05, print_results=True, show_plot=True)¶
Performs the Kolmogorov-Smirnov goodness of fit test to determine whether we can accept or reject the hypothesis that the data is from the specified distribution at the specified level of significance. This method is not a means of comparing distributions (which can be done with AICc, BIC, and AD), but instead allows us to accept or reject a hypothesis that data come from a distribution.
Inputs: distribution - a distribution object created using the reliability.Distributions module data - an array or list of data that are hypothesised to come from the distribution significance - This is the complement of confidence. 0.05 significance is the same as 95% confidence. Must be between 0 and 0.5. Default is 0.05. print_results - if True the results will be printed. Default is True show_plot - if True a plot of the distribution CDF and empirical CDF will be shown. Default is True.
Outputs: KS_statistic - the Kolmogorov-Smirnov statistic KS_critical_value - the Kolmogorov-Smirnov critical value hypothesis - ‘ACCEPT’ or ‘REJECT’. If KS_statistic < KS_critical_value then we can accept the hypothesis that the data is from the specified distribution