Dear Julian. Thank you so much for your very detailed explanation. It provided a far greater insight into the CLT than I had hoped for and is much appreciated. On the other point, it will be interesting to see how far the call for retirement of the p-value threshold will progress. Perhaps the vast majority of scientists will again misunderstand how to apply that concept should it indeed be adopted as, I think for now, the guidelines focus not on what we should do but rather what we shouldn't. My background is not in statistics - indeed, in my day, it was hardly taught at University and I have largely gained an understanding through my own experience over the years with JMP being largely instrumental in my having become the go-to-guy for any statistical queries in my area of research. Personally, I cannot imagine investigating data with any other software package. I certainly enjoyed the humour of the other posts though and will keep them in my back pocket for cocktail parties ;-> ... View more

Although I enjoy a bit of wit, I thought it would have been tempered with at least some degree of serious contemplation. The matter of significance thresholds is a serious one which I believed protagonists of JMP/SAS would have provided a modicum of guidance. I would suggest reading the editorial in The American Statistician Vol 73;S1 (2019). The essence of that article recommends: Not to base one's conclusions solely on whether an association or effect was found to be 'statistically significant' (i.e., the p-value passed some arbitrary threshold such as p < 0.05). Not to believe that an association or effect exists just because it was 'statistically significant'. Not to believe that an association or effect is absent just because it was not 'statistically significant'. Not to believe that your p-value gives the probability that chance alone produced the observed association or effect or the probability that your test hypothesis is true. Not to conclude anything about scientific or practical importance based on statistical significance (or lack thereof). The journal further confirms that a statement  “be sent to the editor-in-chief of every journal in the natural, behavioral and social sciences for forwarding to their respective editorial boards and stables of manuscript reviewers."   I am not disagreeing with these recommendations but, as I pointed out, this goes further than one's interpretation of significance levels in final inferential statistical testing to dictating which methods are used to arrive at that answer. ... View more

Well, thanks, I'll build that into my presentation on the subject - it'll be sure to get a laugh! ... View more

Just to clarify, I know one can do sample size analysis based on variance, power, etc. to avoid Type I errors, but is there a sample size where non-parametric testing is avoided by default? ... View more

I realise this is an old discussion, but I found it most helpful. I have been able to successfully implement it but have the following question: For evaluating the impact of both variables on the outcome variable, I assume one must test each separately, using ranks created using both but alternating them between blocking and dependent roles. However, how does one determine interaction between these two variables? Luckily I was able to transform my data successfully and perform a two way ANOVA using the Fit Model platform. Is this a limitation of the Friedman test? ... View more