NONPARAMETRIC PREDICTIVE INFERENCE FOR REPRODUCIBILITY OF TWO BASIC TESTS BASED ON ORDER STATISTICS

Reproducibility of statistical hypothesis tests is an issue of major importance in applied statistics: if the test were repeated, would the same overall conclusion be reached, that is rejection or non-rejection of the null hypothesis? Nonparametric predictive inference (NPI) provides a natural framework for such inferences, as its explicitly predictive nature fits well with the core problem formulation of a repeat of the test in the future. NPI is a frequentist statistics method using relatively few assumptions, made possible by the use of lower and upper probabilities. For inference on reproducibility of statistical tests, NPI provides lower and upper reproducibility probabilities (RP). In this paper, the NPI-RP method is presented for two basic tests using order statistics, namely a test for a specific value for a population quantile and a precedence test for comparison of data from two populations, as typically used for experiments involving lifetime data if one wishes to conclude before all observations are available.