statistical-analysis

A/B testing analysis uses a two-sample t-test to compare numeric samples, reporting p-values for significance and Cohen's d to quantify the effect size. This validates whether differences between variants are statistically meaningful.

Hypothesis testing supports t-tests for comparing means, chi-square for categorical data, and ANOVA for comparing multiple groups. These tests help validate findings and determine whether observed differences across datasets are statistically significant.

Confidence intervals are calculated to estimate the range of uncertainty around a parameter, while normality checks verify distribution assumptions. Both are essential steps in experimental design to ensure your statistical inference is valid.

Yes, the statistical analysis workflow is built on numpy and scipy, leveraging these dependencies for efficient numerical computation. It applies scipy's statistical functions to execute t-tests, ANOVA, and distribution analysis across datasets.

Experimental design is best approached by defining hypothesis tests upfront, then using correlation analysis to measure relationships between variables. Interpreting these correlations alongside confidence intervals quantifies uncertainty in your data-driven decisions.

ANOVA should be used instead of multiple t-tests when comparing three or more groups, as it controls the error rate across comparisons. This reduces guesswork in data-driven decisions while providing accurate effect size calculations.