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Coefficient of Variation Calculator

Coefficient of variation calculator

Measure relative spread as CV = standard deviation / mean.

InputsStatistics2 fieldsLive

Result

40%

Relative variability based on population standard deviation.

Live update

relative spread

CV intensity

0% to 100%+
Advanced options
Flow
  • Enter dataset values as numbers.
  • Choose sample or population standard deviation mode.
  • Use CV percentage to compare relative variation across groups.
Example

Worked example: 2,4,4,4,5,5,7,9 (population mode)

  1. 1 Mean = 5.0 and population SD = 2.0
  2. 2 CV = (2.0 / 5.0) × 100
  3. 3 CV = 40%

Coefficient of variation is 40%.

How
  1. Enter dataset values as numbers.
  2. Choose sample or population standard deviation mode.
  3. Use CV percentage to compare relative variation across groups.
Avoid
  • Using CV when mean is near zero, which destabilizes interpretation.
  • Comparing CV across datasets with different data quality or sampling assumptions.
  • Confusing CV with variance units rather than percentage form.
Checks

Best fit

Coefficient of Variation Calculator is built for compute coefficient of variation (cv) to compare relative variability across datasets. If Coefficient of Variation Calculator does not match the input scope, compare the answer with a second method.

Input check

Match the entered values to this rule before copying the answer: CV (%) = (standard deviation / |mean|) × 100.

Sanity check

For Coefficient of Variation Calculator, use the worked example as a quick benchmark: Coefficient of variation is 40%. If the coefficient of variation calculator answer is far away, check whether an input, unit, or mode changed.

Before copying

Review this common issue first: using cv when mean is near zero, which destabilizes interpretation.

FAQ
When is CV useful?

CV is useful when comparing variability across different scales.

Can CV be negative?

No. This implementation uses absolute mean in the denominator.

Should I use sample or population mode?

Use sample for sampled data and population when the dataset is complete.

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