What does a positive skewness value mean?

A positive value indicates the distribution has a longer or fatter right tail — most values are clustered to the left of the mean with a few high outliers pulling it right.

What is the difference between Pearson's and Fisher's formula?

Pearson's moment coefficient divides by n (biased). Fisher's adjusted formula uses a correction factor (n/((n-1)(n-2))) making it unbiased for small samples. Fisher's is preferred in most statistical software.

What skewness value is considered symmetric?

A perfectly symmetric distribution has skewness = 0. Values between -0.5 and +0.5 are generally considered approximately symmetric; beyond ±1 is highly skewed.

How many data points do I need?

At least 3 values are required. Fisher's formula requires at least 3 points (it divides by n-2). For reliable skewness estimates, 20+ data points are recommended.

Does this tool store my data?

No. All computation happens in your browser. Your data is never sent to any server.

Skewness Calculator

Name: Skewness Calculator
Availability: InStock
Author: Nham Vu

Enter a dataset and instantly get the skewness value using Pearson's moment or Fisher's adjusted formula.

Dataset Input

Enter numbers (comma, space, or newline separated)

Formula

Fisher's adjusted Unbiased, recommended for small samples Pearson's moment coefficient Classic biased formula (divides by n)

Load sample data:

Enter your dataset and click Calculate

Summary

Enter a dataset and instantly get the skewness value using Pearson's moment or Fisher's adjusted formula.

How it works

Paste or type your numbers separated by commas, spaces, or newlines.
Select the formula: Pearson's moment coefficient or Fisher's adjusted skewness.
Click "Calculate" to see the skewness value along with mean, median, variance, and standard deviation.
Use the interpretation guide to understand whether the distribution is symmetric, positively skewed, or negatively skewed.
Copy the result or adjust the dataset and recalculate as needed.

Use cases

Assess the symmetry of exam scores or survey responses.
Check financial return distributions for tail risk analysis.
Validate assumptions before applying parametric statistical tests.
Explore environmental or scientific measurement datasets for outliers.
Quality control: detect process drift in manufacturing measurements.
Data science: understand feature distributions before model training.

Frequently Asked Questions

Last updated: 2026-06-13 · Reviewed by Nham Vu