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We firstly perform single-dimension T test and Kolmogorov Smirnov test to test whether male and female datasets are drawn from the same distribution.
Finally, we used a t test to test whether slopes were significantly different.
We next used a differential t test to test whether a gene is differentially expressed between the two relevant conditions.
Furthermore, we used the Hotelling Williams test to test whether a learned metric resulted in a significantly different CPCC from the baseline (Steiger 1980).
As an extra check, we also fitted a negative binomial model and used the Lagrange multiplier test to test whether the ancillary parameter equalled zero.
In order to test associations, we did a chi-square goodness of fit test to test whether the observed proportions for 'agree' or 'disagree' were different when a given question concerned policy versus when the same question concerned clinical practice.
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When dealing with classical spike train analysis, the practitioner often performs goodness-of-fit tests to test whether the observed process is a Poisson process, for instance, or if it obeys another type of probabilistic model (Yana et al. in Biophys. J. 46(3):323–330, 1984; Brown et al. in Neural Comput.
We used group t-test or Mann–Whitney U-test to test whether pesticide levels differed significantly by ethnicity.
Comparison of the difference is usually done by performing a multivariate F-test to test whether two populations are distributed differently.
Each connectivity parameter was subsequently submitted to 1-sample t-test to test whether the connectivity parameters were significant across all 34 participants.
We used a z-test to test whether the proportion of shared orthologous groupings was different between our two comparisons (A. mellifera vs. N a. vitripennis and D. melanogaster vs. M. domestica).
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