Statistics · Hypothesis testing

Jarque–Bera Normality Test Calculator

Screen normality through sample skewness and kurtosis with the Jarque–Bera statistic.

Statistics · Hypothesis testing

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Private in-browser calculation · explicit assumptions
The test measures a particular discrepancy between entered observations and a fitted or specified null pattern.
  1. 1EnterProvide the known values
  2. 2CalculateResults update automatically
  3. 3VerifyReview the details and units
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Understand the hypothesis test

Treat model checks as evidence, not proof

Jarque–Bera Normality Test. A diagnostic test compresses one kind of departure into a statistic. Different checks emphasize tails, shape, cumulative gaps, or ordering.

Test statistic and reference model

JB=n[S²/6+(K−3)²/24], asymptotically χ²(2).

Failure to reject normality is not proof of normality, especially in small samples.

Responsible interpretation

Failure to reject is not proof that the data are normal, random, independent, or well modeled. Small samples may have little power and huge samples may flag minor departures.

Required assumption: The chi-square reference is asymptotic and the test focuses only on skewness and kurtosis departures.

Quick guide

How to use this calculator

  1. Enter the observations, probabilities, model parameters, or summary statistics requested by the visible labels.
  2. Keep every value on the same scale and confirm that the selected sampling relationship, distribution, and tail convention match the question you are investigating.
  3. Read the result together with its assumptions and interpretation. Statistical output summarizes uncertainty under a model; it does not repair biased data or establish causation.

Calculation method

How the jarque–bera normality test calculator works

JB=n[S²/6+(K−3)²/24], asymptotically χ²(2).

Failure to reject normality is not proof of normality, especially in small samples.

Worked example

Jarque–Bera Normality Test example

A symmetric light-tailed sample produces small skewness and excess-kurtosis contributions.

JB=n[S²/6+(K−3)²/24], asymptotically χ²(2).

Supported inputs

Precision and limits

Model and design

The chi-square reference is asymptotic and the test focuses only on skewness and kurtosis departures.

Numerical scope

Inputs use double-precision numerical methods with guarded domains. Datasets accept up to 10,000 finite plain-decimal values. Extremely large parameters or probabilities deep in a numerical tail may require specialist statistical software.

Interpretation

Failure to reject normality is not proof of normality, especially in small samples.

Decision boundary

The calculator does not validate how data were collected, diagnose dependence or bias, choose a scientifically meaningful effect, or replace review by a qualified statistician for consequential research, medical, regulatory, safety, or policy decisions.

Privacy

Entered values and calculated results stay in this browser and are not sent to an analytics service.

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