Agent skill · data analytics · anthropics
statistical-analysis
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
Why this skill is useful
Provides specific statistical methods and Python code snippets for trend analysis and forecasting that the AI wouldn't reliably generate on its own.
What it needs
About 6k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
Statistical Analysis Skill Descriptive statistics, trend analysis, outlier detection, hypothesis testing, and guidance on when to be cautious about statistical claims. Descriptive Statistics Methodology Central Tendency Choose the right measure of center based on the data: Situation Use Why --- --- --- Symmetric distribution, no outliers Mean Most efficient estimator Skewed distribution Median Robust to outliers Categorical or ordinal data Mode Only option for non-numeric Highly skewed with outliers (e.g., revenue per user) Median + mean Report both; the gap shows skew Always report mean and median together for business metrics. If they diverge significantly, the data is skewed and the mean alone is misleading. Spread and Variability Standard deviation: How far values typically fall from the mean. Use with normally distributed data. Interquartile range (IQR): Distance from p25 to p75. Robust to outliers. Use with skewed data. Coefficient of variation (CV): StdDev / Mean. Use to compare variability across metrics with different scales. Range: Max minus min. Sensitive to outliers but gives a quick sense of data extent. Percentiles for Business Context Report key percentiles to tell a richer story than mean alone: Example narrative: "The median session duration is 4.2 minutes, but the top 10% of users spend over 22 minutes per session, pulling the mean up to 7.8 minutes." Describing Distributions Characterize every numeric distribution you analyze: Shape: Normal, right-skewed, left-skewed, bimodal, uniform, heavy-tailed Center: Mean and median (and the gap between them) Spread: Standard deviation or IQR Outliers: How many and how extreme Bounds: Is there a natural floor (zero) or ceiling (100%)? Trend Analysis and Forecasting Identifying Trends Moving averages to smooth noise: Period-over-period comparison: Week-over-week (WoW): Compare to same day last week Month-over-month (MoM): Compare to same month prior Year-over-year (YoY): Gold standard for seasonal businesses …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/statistical-analysis