Standard Deviation Calculator

Calculate standard deviation, variance, mean, and other statistical measures for your dataset. Supports both population and sample standard deviation calculations.

Data Input

Quick Presets

Data Summary

Number of values: 7
Range: 10.00 to 40.00
First few values: 10.00, 15.00, 20.00, 25.00, 30.00...

Statistical Results

Primary Statistics

10.8012
Standard Deviation (s)
116.6667
Variance (s²)
25.0000
Mean (μ)
7
Count (n)

Descriptive Statistics

25.0000
Median
None
Mode
30.0000
Range
43.2049%
Coefficient of Variation

Quartiles & Percentiles

17.5000
Q1 (25th percentile)
25.0000
Q2 (50th percentile)
32.5000
Q3 (75th percentile)

Additional Statistics

0.0000
Skewness
-1.2000
Kurtosis
15.0000
Interquartile Range (IQR)
4.0825
Standard Error

Confidence Intervals (95%)

15.0102 to 34.9898
95% Confidence Interval for Mean

Distribution Analysis

Distribution Shape:
Approximately symmetric, light-tailed (platykurtic)
Outliers (using IQR method):
No outliers detected

How Standard Deviation Works

Population Standard Deviation (σ)

Formula:

σ = √[Σ(x - μ)² / N]

Where:

  • σ = population standard deviation
  • x = each value in the dataset
  • μ = population mean
  • N = total number of values

Sample Standard Deviation (s)

Formula:

s = √[Σ(x - x̄)² / (n - 1)]

Where:

  • s = sample standard deviation
  • x = each value in the sample
  • x̄ = sample mean
  • n = sample size

Interpreting Standard Deviation

What Standard Deviation Tells Us

Low Standard Deviation:
Data points are close to the mean (less variability)
High Standard Deviation:
Data points are spread out from the mean (more variability)
Zero Standard Deviation:
All data points are identical

68-95-99.7 Rule (Normal Distribution)

68% of data:
Within 1 standard deviation of the mean
95% of data:
Within 2 standard deviations of the mean
99.7% of data:
Within 3 standard deviations of the mean

Example Calculations

Example 1: Test Scores

Data: 85, 90, 78, 92, 88, 76, 95, 89
Mean: 86.625
Sample Standard Deviation: 6.93
Population Standard Deviation: 6.50
Interpretation: Most scores are within 7 points of the average

Example 2: Heights (cm)

Data: 165, 170, 175, 168, 172, 169, 174, 171
Mean: 170.5 cm
Sample Standard Deviation: 3.46 cm
Population Standard Deviation: 3.24 cm
Interpretation: Heights are fairly consistent, varying by about 3-4 cm

Frequently Asked Questions

When should I use population vs. sample standard deviation?

Use population standard deviation when you have data for the entire population. Use sample standard deviation when you have a sample from a larger population and want to estimate the population's standard deviation.

What's the difference between standard deviation and variance?

Variance is the average of squared differences from the mean, while standard deviation is the square root of variance. Standard deviation is in the same units as your data, making it easier to interpret.

How do outliers affect standard deviation?

Outliers can significantly increase standard deviation because the calculation involves squared differences. Consider identifying and potentially removing outliers for a more representative measure of variability.