vamseeachanta Central Tendency (+3)
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:
p1: Bottom 1% (floor / minimum typical value)
p5: Low end of normal range
p25: First quartile
p50: Median (typical user)
p75: Third quartile
p90: Top 10% / power users
p95: High end of normal range
p99: Top 1% / extreme users
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%)?