Measure relative variability as a standardized percentage of the mean.
Last updated: March 2026
| Field/Context | CV Range (Typical) | Interpretation | Action/Assessment |
|---|---|---|---|
| Quality Control | < 5% | Excellent consistency | Process is stable |
| Manufacturing | 5–10% | Good consistency | Acceptable variation |
| Biological/Medical | 15–30% | Moderate variability | Normal for living systems |
| Finance/Investment | > 30% | High variability/risk | Volatile asset |
The Coefficient of Variation (CV) is a standardized measure of dispersion that expresses the standard deviation as a percentage of the mean. It allows for meaningful comparison of variability between datasets with different scales or units.
Unlike the standard deviation (which is in the same units as the data), CV is unitless, making it ideal for comparing the relative variability across different datasets. For example, comparing the variability of test scores and household incomes directly using standard deviation would be misleading because they have different scales.
The CV is calculated as: CV = (σ / |μ|) × 100%, where σ is the standard deviation and μ is the mean. A lower CV indicates less variability relative to the mean, while a higher CV indicates greater relative variability.
Two investment portfolios—which is more variable?
Standard Deviation is in the same units as the data and shows absolute variability. CV is unitless and shows relative variability as a percentage of the mean, enabling comparisons across different scales.
Yes, CV can exceed 100%. This indicates that the standard deviation is larger than the mean, suggesting very high variability relative to the central tendency.
If the mean is zero, CV is undefined (division by zero). For negative means, use the absolute value of the mean in the denominator to calculate CV.
CV is better for comparing datasets on different scales. For example, comparing stock price volatility (ranging $10-$1000) with commodity prices requires CV to account for scale.
Not always—it depends on context. In quality control, low CV is desirable. In investment returns, it depends on your risk tolerance and goal.
CV is used in quality control to monitor process consistency, in finance to compare investment risk, and in scientific research to assess measurement precision across different scales.
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