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Coefficient of Variation Calculator

Statistics

Calculate the coefficient of variation (CV) from a mean and standard deviation instantly. Compare relative variability across datasets with different units or scales.

-1,000,0001,000,000
01,000,000

Coefficient of Variation

20.00%

This calculator computes your Coefficient of Variation from the values you enter.

Inputs
MeanStandard Deviation
Outputs
Coefficient of Variation

What is a Coefficient of Variation?

The Coefficient of Variation Calculator computes CV โ€” a standardized, unitless measure of relative variability โ€” from a mean and standard deviation you already have. Enter both values, and the calculator instantly returns the coefficient of variation as a percentage, letting you compare consistency across datasets regardless of their scale or units.

Standard deviation alone is often misleading when comparing variability across different datasets, because it's expressed in the original units and scale of the data. The coefficient of variation solves this by expressing standard deviation as a proportion of the mean, producing a single normalized percentage you can compare fairly across completely different metrics.

If you have raw data rather than a pre-computed mean and standard deviation, use the Standard Deviation Calculator first to generate both inputs, then bring them here.

How to use this Coefficient of Variation calculator

  1. Enter the mean โ€” the average value of your dataset, which you may have computed with the Standard Deviation Calculator or received from another source.

  2. Enter the standard deviation โ€” either the population or sample standard deviation corresponding to that same dataset.

  3. Read the coefficient of variation โ€” the highlighted result shows CV as a percentage, ready to compare against other datasets or against field-specific benchmarks.

  4. Adjust and compare โ€” change either input to instantly see how CV responds, useful for quickly comparing multiple scenarios side by side.

  5. Check the step-by-step breakdown โ€” expand the calculation steps to see the exact formula substitution behind your result.

Formula & Methodology

Coefficient of variation formula:
CV = (ฯƒ / ฮผ) ร— 100%

Variable definitions:
- ฯƒ โ€” standard deviation
- ฮผ โ€” mean

Worked example:

Dataset A: mean = 50, standard deviation = 10. Dataset B: mean = 5,000, standard deviation = 750.

Dataset A: CV = (10 / 50) ร— 100 = 20%

Dataset B: CV = (750 / 5,000) ร— 100 = 15%

Even though Dataset B has a much larger raw standard deviation (750 vs. 10), its coefficient of variation is actually lower โ€” meaning Dataset B is relatively more consistent around its mean than Dataset A, once you account for the very different scales. This is the exact comparison that raw standard deviation alone cannot make fairly.

Note: CV is undefined when the mean is zero, and becomes unstable and less meaningful when the mean is very close to zero. It is most appropriate for ratio-scale data with a genuinely meaningful, non-zero mean, such as weights, prices, durations, or measurement values.

Frequently Asked Questions

The coefficient of variation (CV) expresses standard deviation as a percentage of the mean, which allows you to compare the relative variability of two or more datasets even when they use different units or wildly different scales. A CV of 10% on a dataset with a mean of 5 and a CV of 10% on a dataset with a mean of 5,000 represent the same relative spread, even though their raw standard deviations look completely different.
CV is calculated as the standard deviation divided by the mean, then multiplied by 100 to express it as a percentage: CV = (ฯƒ / ฮผ) ร— 100%. For example, a dataset with a mean of 50 and a standard deviation of 10 has a CV of (10/50) ร— 100 = 20%.
There's no universal threshold, but as a general rule of thumb, a CV below 15% is often considered low variability, 15โ€“30% moderate, and above 30% high variability โ€” though acceptable ranges vary enormously by field. In laboratory science, a CV under 5% is often required for assay precision, while in financial returns, CVs well above 30% are common and expected.
Standard deviation is expressed in the same units as the original data, which makes it meaningless to compare across datasets with different units (like comparing a standard deviation in dollars to one in kilograms) or even the same units at wildly different scales (comparing variability in company revenue of $10K vs. $10B). CV solves this by normalizing variability relative to the mean, producing a unitless percentage that's directly comparable.
In practice, CV is typically reported as a positive percentage using the absolute value of both the standard deviation (which is always non-negative) and the mean. This calculator uses absolute values for both, so a negative mean does not produce a negative or nonsensical CV โ€” the underlying calculation always yields a meaningful, comparable percentage.
The coefficient of variation formula divides by the mean, so a mean of exactly zero makes CV mathematically undefined (division by zero). A mean very close to zero can also produce an extremely large, unstable CV that doesn't meaningfully reflect variability โ€” in these cases, CV is not an appropriate metric, and you should use standard deviation directly instead.
In investing, CV is used to compare the risk-adjusted volatility of different assets or portfolios, since it normalizes the standard deviation of returns by the expected (mean) return. An investment with a lower CV delivers more consistent returns relative to its average return, which is useful when comparing assets with very different average return levels.
Quality control teams use CV to monitor process consistency across production batches or measurement instruments, especially when comparing variability across products measured on different scales (like weight in grams versus volume in milliliters). A rising CV over time often signals a process drifting out of control, even if the raw standard deviation alone doesn't look alarming.
Standard deviation measures absolute variability in the original units of the data, while CV expresses that same variability relative to the mean, as a percentage. If you only have the mean and standard deviation (rather than the raw dataset), use this calculator directly; if you have raw data and need to compute standard deviation first, use the [Standard Deviation Calculator](/standard-deviation-calculator/).
No โ€” CV is a purely descriptive ratio and doesn't require any assumption about the underlying distribution shape. However, it is most meaningful and most commonly used for data measured on a ratio scale with a meaningful, non-zero mean (like weights, prices, or times), rather than data that can naturally take negative values or lacks a true zero point.
A z-score measures how far a single data point is from the mean in standard deviation units, while CV measures the overall relative spread of an entire dataset around its mean. Use the [Z-Score Calculator](/z-score-calculator/) to evaluate one specific value's standing, and this calculator to evaluate overall dataset consistency.
Yes โ€” this is one of CV's main strengths. Because CV is unitless (a percentage), it lets you fairly compare the relative variability of height (in cm) against weight (in kg) or any other differently-scaled metrics, something raw standard deviation comparisons cannot do.
Also known as
CV calculatorcoefficient of variation formularelative standard deviation calculatorRSD calculatorvariability ratio calculator