Data Analysis

A/B test analysis requires framing a hypothesis, selecting the right statistical test, and calculating p-values alongside effect sizes. This workflow checks sample size, confounders, and quantifies uncertainty to deliver decision-ready insights.

During cohort analysis, you must check for Simpson's paradox, survivorship bias, and multiple comparisons. Identifying these statistical pitfalls ensures your retention metrics and derived insights are not skewed by confounding variables.

Significant p-values without real effect often indicate insufficient sample size or multiple comparison errors. Evaluating effect size and confidence intervals alongside p-values provides a more accurate measure of the actual impact.

Data quality assessment for churn analysis involves checking raw datasets for missing values, confounders, and biases. Applying a systematic analytical workflow validates the dataset before calculating retention metrics and uncertainty.

You need to quantify uncertainty in business metrics whenever making data-driven decisions from raw data. Calculating confidence intervals and effect sizes ensures your regression-based predictions and analyses account for variance.

Yes, the systematic data analysis workflow supports regression-based predictions across business metrics. It guides you through hypothesis development, method selection, and uncertainty quantification to derive actionable insights.