statistical-analysis

Hypothesis testing evaluates whether observed differences in business metrics are statistically meaningful by defining H0/H1, setting an alpha significance level, and interpreting p-values. It distinguishes practical versus statistical significance while checking sample-size adequacy.

Outlier detection in product analytics identifies anomalies using z-score, IQR, or percentile thresholds. It distinguishes isolated point anomalies from sustained change points, helping you filter messy data before computing central tendency and spread metrics.

Descriptive reporting summarizes skewed distributions by selecting appropriate measures of center and spread such as median and IQR instead of mean and standard deviation. It interprets distribution shape, percentiles, and coefficient of variation for business metrics.

Statistical analysis supports trend evaluation and simple forecasting by computing WoW, MoM, or YoY growth rates. It checks for seasonality patterns and produces forecasts with uncertainty ranges to guide product decisions.

A/B testing safeguards prevent common statistical fallacies by applying corrections for multiple comparisons and explicitly separating correlation from causation. It ensures observed metric differences reflect true effects rather than random noise or confounding variables.