Standard Deviation Formula
Standard deviation measures how spread out numbers are from the mean. A low SD means data points cluster near the average; a high SD means they're spread out.
Population Standard Deviation
σ = √[Σ(xi - μ)² / N]
Where:
- σ = Standard deviation
- xi = Each data point
- μ = Mean (average)
- N = Number of data points
Step-by-Step:
- 1
Find the mean
Sum all values and divide by count.
- 2
Subtract mean from each value
Calculate (xi - μ) for each data point.
- 3
Square each difference
Square all the deviations.
- 4
Average and square root
Sum the squares, divide by N, take square root.
Worked Examples:
Test scores
Data: 85, 90, 78, 92, 88
Result: SD = 4.86
Mean = 86.6. Deviations: -1.6, 3.4, -8.6, 5.4, 1.4. Squared sum = 114.8. Variance = 22.96. SD = √22.96 = 4.79