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Sample Excess Kurtosis Calculator

Compute bias-corrected Fisher excess kurtosis from sample observations.

Data stays on this deviceConvention explained below
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Data explorer

Check the dataset and sample convention before interpreting results.

See the method

Add your observations

Runs on your device

Your statistical summary

Your result

Let’s calculate.

Use the Calculate button to see your result.

Review the method below for assumptions and conventions.

How to use this tool

  1. Enter observations.
  2. Select Calculate to view the result.
  3. Check the method and assumptions below before using the result.

The method, explained

Let g2=m4/m2²−3. Corrected excess=[(n−1)/((n−2)(n−3))]×[(n+1)g2+6].

A WORKED EXAMPLE

Using observations = 1, 2, 3, 4, 5, the result is -1.2. Change these example inputs to match your task; use the method above to check each step.

What to keep in mind

Fisher excess convention: normal-distribution population excess is zero. Sample corrections vary across software settings. A descriptive or probability calculation under the stated assumptions. Check the sample design and model before interpreting results.

Common questions

Which inputs does this calculation need?

Compute bias-corrected Fisher excess kurtosis from sample observations. Enter observations. The filled example is editable and is not a saved personal record.

How should I interpret the result?

Fisher excess convention: normal-distribution population excess is zero. Sample corrections vary across software settings. A descriptive or probability calculation under the stated assumptions. Check the sample design and model before interpreting results.

Methodology maintained by ClarityKit. How these tools are built and checked.