statistical-analysis
Statistical methods for descriptive analysis, trend detection, outlier identification, and hypothesis testing with practical business interpretation.
- Covers descriptive statistics (mean, median, percentiles, spread measures), trend analysis with moving averages and period-over-period comparisons, and simple forecasting methods for business analysts
- Includes three outlier detection approaches (z-score, IQR, percentile methods) with guidance on investigating and handling anomalies rather than automatic removal
- Provides hypothesis testing framework for A/B tests and segment comparisons, emphasizing practical significance, effect size, and confidence intervals alongside p-values
- Addresses common statistical pitfalls: correlation vs. causation, multiple comparisons, Simpson's paradox, survivorship bias, and false precision in forecasts
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.
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