What problem does it solve?
Provides clear, practical guidance to summarize numeric data, identify trends and anomalies, and evaluate whether observed differences are statistically meaningful so non-expert analysts can make defensible decisions from datasets.
Core Features & Use Cases
- Descriptive statistics: recommended measures of center and spread (mean, median, IQR, standard deviation) and relevant percentiles for business reporting.
- Trend analysis and simple forecasting: moving averages, period-over-period comparisons, seasonality checks, and naïve/seasonal forecasts for short-term planning.
- Outlier and anomaly detection: z-score, IQR, and percentile methods with investigation and handling guidance rather than blind removal.
- Hypothesis testing guidance: when to apply t-tests, chi-squared tests, ANOVA, nonparametric alternatives, plus advice on effect sizes, confidence intervals, and sample size considerations.
- Use cases: A/B test evaluation, monthly retention trend summaries, fraud/outlier investigation in transaction data, and executive-ready summaries that balance statistical and practical significance.
Quick Start
Use the statistical-analysis skill to compute descriptive summaries, detect outliers, and run an appropriate hypothesis test on the uploaded dataset to support a business decision.
