I have one request/recommendation for a future release of JMP. In the one-sample equivalence test platform in JMP 16.0.0, I noticed that there is no correction for the non-parametric test case. Yes I can, in essence conduct the test using the one-sample T-test platform: and by running the Test mean option 2X and picking off the appropriate tests as shown in the graphic below:
But I can’t get the Equivalence region nor can I get the TOST framework formalized in this same way which is what I like about the Test Equivalence Platform for the normality assumed case.
So formally the request is for a non-parametric version of the TOST for the one-sample test.
Note that for the 2-sample case I can compute a non-parametric confidence interval on the median difference and compare that to the practical difference region and effectively run the TOST in this way (e.g. Hodges-Lehmann confidence interval on median difference using Wilcoxon all-pairs NP test). This is just the “confidence interval version” of the TOST. So this actually works quite well for the 2-sample case without any need for an additional feature in the Equivalence Test platform in Fit Y by X.
But in the 1-sample case, in the Distribution platform, I cannot do the same, why? The only way I know of doing something similar to this would be using Custom Quantiles in JMP's Distribution platform, and that does provide a non-parametric estimate (albeit not the Hodges-Lehmann one, and I have trouble specifying the exact desired coverage for Confidence level of the TOST which is a standard 90% for a 5% significance level).
So (lower-priority) an additional feature altogether could be, the calculation of a non-parametric confidence interval estimate using Hodges-Lehmann on the median in the Distribution Platform. Or perhaps a boot-strapped confidence interval (which you can do for almost every table already in JMP Pro with Right Click > Bootstrap on data table output).
