<p dir="auto">Running scipy.stats.kendalltau on attached data gives tau = 0.12017 and two-tailed p = 0.0666</p>
<p dir="auto">Running same test (kenall tau-b) in R (cor.test), SPSS (NONPAR CORR) and the Excel Real-Statistics addin gives same value of tau but p = 0.099 in all 3 packages.<br>
<a href="https://github.com/scipy/scipy/files/333596/fmmnonzero.txt">fmmnonzero.txt</a></p>
<p dir="auto">The null statistics for kendall's τ is <a href="https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient#Significance_tests" rel="nofollow">very different</a> in the case of ties. Presently, the code computes τ and then applies the null statistics for the tieless case to the general case, leaving people <a href="http://stats.stackexchange.com/questions/71026/kendall-tau-b-correlation-coefficient-becomes-more-significant-with-many-additio" rel="nofollow">really puzzled</a>.</p>
<p dir="auto">I have created a <a href="https://github.com/scipy/scipy/pull/5754" data-hovercard-type="pull_request" data-hovercard-url="/scipy/scipy/pull/5754/hovercard">pull request</a> that improves performance and fixes a few problems, putting again the code in line with that we developed at the LAW (which was used in 2009). In doing so, I set up things so that a p-value of NaN is returned in case of ties, as the present situation is most dangerous.</p>
<p dir="auto">I'm ready to help to modify the code so that it computes the pieces of information that the real null statistic needs, but I need a statistician to check the theoretical part. (IANS)</p>