My steps into deeper statistical territory
Is a shared, open standard for data quality. You write profiles for your data and columns (the expectations), which can be tested. In some ways it is a framework for standardizing tests, on your data.
For large scale surveys, handles sampling, sample weighting, population parameters estimation, categorical data analysis and small area estimation.
An open-source statistical package written in Python 3 and based mostly on Pandas and NumPy. ANOVAs, Pairwise post-hocs tests, robust, partial, distance and repeated measures correlations Linear/logistic regression and mediation analysis, Bayes Factors, Multivariate tests Reliability and consistency, Effect sizes and power analysis, Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient, Circular statistics, Chi-squared tests, Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation...