statistics

I use distribution plots to decide whether a feature is stable enough to model, whether it needs transformation, or whether data drift is already happening. Seaborn makes it easy to compare classes, cohorts, or time windows. The visual check usually c

I use hypothesis testing to quantify whether observed differences are likely noise or signal, but I keep the business context attached. A tiny p-value without practical effect size is not a win. The code should make assumptions visible: sample sizes,

Experiment analysis should not stop at a binary win or lose label. I calculate uplift, confidence intervals, and guardrail metrics like latency or refund rate before recommending rollout. The point of the analysis is decision quality, not statistical