data-statistical-analysis

What problem does it solve?

Provides a structured, reproducible approach to analyze business metrics and workspace datasets by producing descriptive statistics, identifying trends and anomalies, and validating observed changes with hypothesis tests so teams make informed, cautious decisions.

Core Features & Use Cases

• Descriptive statistics: Guidance on mean, median, IQR, percentiles, dispersion, and when to prefer robust measures for skewed business metrics like MRR.
• Trend & time-series analysis: Practical methods such as moving averages, period-over-period comparisons, and simple forecasting baselines for operational monitoring.
• Outlier and anomaly detection: Multiple approaches (Z-score, IQR, percentile) and a decision framework for investigating and treating outliers in financial and licensing data.
• Hypothesis testing & experiment analysis: Recommended tests (t-test, chi-square, Mann-Whitney), sample size considerations, effect sizes, and guidance on statistical vs. practical significance for A/B and before/after comparisons.
• Use case: Analyze MRR distribution, detect anomalous drops in license instances, or evaluate whether a product change moved conversion rates with appropriate statistical rigor.

Quick Start

Analyze the dataset to compute descriptive statistics, flag outliers, and run hypothesis tests relevant to your business question.