Summary: This paper develops a nonparametric multi-sample inference for simple-tree alternatives for ranked-set samples. The multi-sample inference provides simultaneous one-sample sign confidence intervals for the population medians. The decision rule compares these intervals to achieve the desired type I error. For the specified upper bounds on the experiment-wise error rates, corresponding individual confidence coefficients are presented. It is shown that the testing procedure is distribution-free.
To achieve the desired confidence coefficients for multi-sample inference, a nonparametric confidence interval is constructed by interpolating the adjacent order statistics. Interpolation coefficients and coverage probabilities are provided, along with the nominal levels.