Summarize, visualize, and understand data before diving into inference.
The mean uses algebraic operations; it's sensitive to outliers. The median is more robust. For symmetric distributions, mean ≈ median ≈ mode.
Variance measures average squared deviation from the mean. Standard deviation has the same units as the data — it's the most widely used spread measure. These connect to the normal distribution via the 68-95-99.7 rule.
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Mean = 40/5 = 8
Deviations: −4, −1, 0, 2, 3. Squared: 16, 1, 0, 4, 9
Variance = 30/5 = 6. SD = √6 ≈ 2.45
Skewness describes asymmetry: right-skewed (mean > median, long right tail), left-skewed (mean < median). Kurtosis describes tail heaviness. The normal distribution has skewness 0 and kurtosis 3 (by convention, "excess kurtosis" = 0).