This article explains the concepts of statistical and cryptographic randomness, compares their characteristics, outlines common tests, and reviews various random number generator types with code examples for PRNG and CSPRNG implementations.
This article demonstrates how to compare the distribution of a variable across control and treatment groups using Python, covering data generation, visual techniques such as boxplots, histograms, KDE, CDF, QQ and ridgeline plots, and a suite of statistical tests including t‑test, SMD, Mann‑Whitney, permutation, chi‑square, Kolmogorov‑Smirnov and ANOVA for both two‑group and multi‑group scenarios.
This article demonstrates how to compare the distribution of a variable across treatment and control groups using Python, covering data simulation, visual techniques such as boxplots, histograms, KDE, CDF, QQ and ridgeline plots, and statistical tests including t‑test, SMD, Mann‑Whitney, permutation, chi‑square, KS and ANOVA.
The article explains why monitoring production ML models is essential to detect data and target drift, describes the open‑source Evidently AI library and its statistical tests, and demonstrates its use on a weather‑forecast example and a plant‑seedling image classification case, including dashboards, code snippets, and visual analysis of drift impact.
