Calculate standard deviation, variance, mean, median, and more from your data set
Quick Answer:Standard deviation measures how spread out data is from the mean. A low standard deviation means data points cluster near the average, while a high value indicates wide spread. Enter comma-separated numbers below for population or sample statistics.
Standard Deviation
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Mean (Average)
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Median
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Variance
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Count
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Min / Max
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Range
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Coefficient of Variation (CV)
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For normally distributed data, the empirical rule (68-95-99.7 rule) states that approximately 68% of data falls within 1 standard deviation of the mean, 95% within 2, and 99.7% within 3. If your coefficient of variation (CV) exceeds 30%, your data has high variability relative to the mean. In 2026, understanding standard deviation is critical for data science, quality control, and financial risk assessment.
Population standard deviation divides by N (the total number of data points) and is used when your data set includes every member of the group. Sample standard deviation divides by N-1 (Bessel's correction) and is used when your data is a subset of a larger population. Using N-1 corrects for the bias that occurs when estimating population variance from a sample.
Step 1: Calculate the mean (average) of your data. Step 2: Subtract the mean from each data point and square the result. Step 3: Sum all squared differences. Step 4: Divide by N (population) or N-1 (sample) to get the variance. Step 5: Take the square root of the variance to get the standard deviation.
The coefficient of variation (CV) is the ratio of the standard deviation to the mean, expressed as a percentage: CV = (StdDev / Mean) x 100. It measures relative variability and allows you to compare the spread of data sets with different units or means. A lower CV indicates less variability relative to the mean. It is commonly used in finance to compare risk across investments.