Enter your dataset as comma-separated numbers and choose between sample or population mode. The Coefficient of Variation Calculator computes the CV (%), standard deviation, mean, and count — giving you a clear picture of how spread out your data is relative to its average.
Coefficient of Variation (CV)
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Standard Deviation
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Mean (Average)
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Number of Values (n)
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Minimum Value
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Maximum Value
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The coefficient of variation (CV) is the ratio of the standard deviation to the mean, expressed as a percentage. It measures relative variability — how spread out the data is compared to its average. A lower CV indicates less variability relative to the mean, while a higher CV indicates more.
The CV formula is: CV (%) = (Standard Deviation / Mean) × 100. For a sample, the standard deviation uses (n−1) in the denominator (Bessel's correction). For a population, it uses n. The mean (μ) is simply the sum of all values divided by the count.
Choose 'Sample' when your data represents a subset drawn from a larger group — this applies Bessel's correction (dividing by n−1) to reduce bias. Choose 'Population' only when your dataset includes every member of the group you're analyzing (dividing by n).
Select your data type (Sample or Population), then enter your numbers separated by commas in the data values field. The calculator immediately computes and displays the CV (%), standard deviation, mean, and count.
There is no universal threshold, as it depends on context. In many fields, a CV below 15% is considered low variability, 15–30% moderate, and above 30% high. In finance, a lower CV generally means less risk relative to return.
The coefficient of variation (CV) and relative standard deviation (RSD) are essentially the same metric — both express standard deviation as a percentage of the mean. The term 'RSD' is more common in analytical chemistry and laboratory sciences, while 'CV' is used broadly in statistics.
The CV is unreliable when the mean is zero or close to zero, since dividing by a near-zero value produces extremely large or undefined results. It is also not meaningful for data on interval scales (like Celsius temperature) where zero does not represent an absence of the quantity.
Yes. A CV greater than 100% simply means the standard deviation is larger than the mean, indicating very high variability relative to the average. This is mathematically valid and can occur with highly dispersed datasets or skewed distributions.