Calculadora de Desvio Padrão

Desvio padrão amostral de até 7 valores.
Created by
Renato Passos, Eng. de Software
Reviewed by
Renato Passos, Eng. de Software

Last updated: Apr 18, 2026

s (amostral)
1,5119

Formula

s = √(Σ(xᵢ−x̄)²/(n−1))

About this calculator

The Standard Deviation Calculator computes the sample standard deviation of up to 7 numeric values. The standard deviation measures data dispersion around the mean, indicating how much values vary. The formula used is s = √(Σ(xᵢ−x̄)²/(n−1)), where x̄ is the mean and n is the sample size. This tool is essential for quick statistical analysis, especially with small datasets.

To use the calculator, enter values separated by commas, spaces, or line breaks. The result shows the sample standard deviation, which uses n−1 in the denominator to correct bias in small samples. This differs from population standard deviation, which divides by n. It is useful for analyzing test scores, lab measurements, or price variations, helping understand data consistency.

Important caveats: the calculator accepts only numeric values (positive or negative), no text. Ensure your data represents a sample, not a whole population. If you have more than 7 values, consider a more robust tool. Remember that standard deviation is sensitive to outliers, so extreme values can inflate the result. To interpret, compare the standard deviation with the mean: a large value indicates high variability.

Frequently asked questions

What is the difference between sample and population standard deviation?

Sample standard deviation uses n−1 in the denominator to correct bias in small samples, while population divides by n. This calculator uses the sample version.

Can I use negative values?

Yes, negative values are accepted. The calculator handles real numbers, including negatives.

How many values can I enter?

Up to 7 values. For more, use other statistical software.

What does a standard deviation of zero mean?

It means all values are identical, no variation.

How to interpret the result?

Compare the standard deviation with the mean. If small relative to the mean, data is consistent; if large, high dispersion.

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