Section 3.2

Measures of Dispersion

It's not just about the center. Understand how spread out your data is using Range, Variance, and Standard Deviation.

1

The Range

Simplicity & Limitation

The simplest measure of dispersion. It is the difference between the largest and smallest values.

Not Resistant

The Range is NOT resistant to outliers. A single extreme value can drastically inflate the range, giving a misleading impression of the data's spread.

2

Standard Deviation

The "Average" Distance

Measures the average distance of each data point from the mean. It is the most common measure of spread.

Population Standard Deviation
Sample Standard Deviation

InteractiveStandard Deviation Calculator

Enter numbers above to calculate standard deviation
3

Variance

Variance is simply the square of the standard deviation. While useful for mathematical modeling, Standard Deviation is preferred for interpretation because its units match the original data.

or
4

The Empirical Rule

RequirementDistribution must be Bell-Shaped (Symmetric).
68%
95%
99.7%

InteractiveThe Empirical Visualizer

-3σ-2σ-1σμ+1σ+2σ+3σ
Area Covered
68.27%
Lower Boundary
-1σ
Upper Boundary
1σ
5

Chebyshev’s Inequality

For Any Distribution

Unlike the Empirical Rule, Chebyshev's Inequality applies to ANY shape of distribution (skewed or symmetric).

k = 2
At least 75%
k = 3
At least 88.9%

InteractiveChebyshev's Inequality Calculator

The Formula: For any distribution, at least of observations lie within standard deviations of the mean.

Common values:

At least this percentage of data lies within standard deviations of the mean:

75.00%

Calculation:

💡 Key Insight: Unlike the Empirical Rule (which only applies to bell-shaped distributions), Chebyshev's Inequality works for any distribution. However, it gives a minimum guarantee, so the actual percentage is often higher.

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