Two-sample test based on model selection and with better performance than the t-test on small sample sizes.
mtest is a Python implementation of the m-test, a two-sample test
based on model selection and described in  and .
Despite their importance in supporting experimental conclusions, standard statistical tests are often inadequate for research areas, like the life sciences, where the typical sample size is small and the test assumptions difficult to verify. In such conditions, standard tests tend to be overly conservative, and fail thus to detect significant effects in the data.
The m-test is a classical statistical test in the sense of defining significance with the conventional bound on Type I errors. On the other hand, it is based on Bayesian model selection, and thus takes into account uncertainty about the model’s parameters, mitigating the problem of small samples size.
The m-test has been found to generally have a higher power (smaller fraction of Type II errors) than a t-test error for small sample sizes (3 to 100 samples).
 Berkes, P., Fiser, J. (2011) A frequentist two-sample test based on Bayesian model selection. arXiv:1104.2826v1
 Berkes, P., Orban, G., Lengyel, M., and Fiser, J. (2011). Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science, 331:6013, 83-87.
mtest ships caches tables of statistics to compute the p-value and
power of new data in the most efficient way. The library is
distributed with tables for p-values (type I error) for N=3,4,…,20
and for N=30,40,…,100. These tables cover the most common cases. New
tables are computed when needed, although completion might take a few
hours. Type II error tables are not included to keep the package size
scripts\compute_basic_tables.py for an example script to
pre-compute tables you might need. The script makes use of the joblib library to distribute the
computations on multiple cores.
mtest is released under the GPL v3. See LICENSE.txt .
Copyright (c) 2011, Pietro Berkes. All rights reserved.