Data Science Blog

A practical guide to non‑parametric bootstrap confidence intervals, how they’re computed, when they’re preferable to parametric intervals, and which parametric intervals are commonly used in A/B test analysis.

A deep dive into statistical power and why running underpowered experiments can lead to misleading results and missed opportunities. Learn how to design tests that deliver reliable insights.

When A/B testing isn't possible, how do we measure success? Learn how Bayesian Structural Time Series (BSTS) allows us to construct synthetic counterfactuals and quantify causal effects with precision.

Why each metric exists, what problem it solves, and how to use it—with simple, concrete examples.

A plain‑English guide to effect size and Cohen’s d: what it is, why it matters, when to use it, and how to interpret it—with a quick example and links to calculators.

How to define a single metric that aligns the team, avoids vanity KPIs, and compounds learning.

A fake results report that shows how to interpret primary, lagging, and guardrail metrics, understand statistical significance (stat sig), and read gray confidence intervals to make decisions.

Consumer-first scorecard with benchmarks and charts. Minimal text, maximum signal.

Learn how to transform business hypotheses into statistically sound experiments that deliver reliable insights. From understanding what we're truly measuring to setting proper risk boundaries.