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Chi-Square Distribution Calculator

Look up the mean and variance of a Chi-Square (χ²) distribution from its degrees of freedom, for goodness-of-fit and independence tests.

Chi-Square Distribution (χ²)

Goodness-of-fit tests and test of independence in contingency tables.

Test Outcome
Mean = 5 • Variance = 10
χ² tests whether observed frequencies match expected theoretical frequencies.
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Calculation Guide & Reference

Chi-Square (χ²) Distribution Mathematics

Chi-square distribution calculator computes the mean and variance of a χ² distribution from its degrees of freedom, for goodness-of-fit and independence tests.

Standardized Mathematical Formula
Mean = df | Variance = 2 × df

The Chi-Square distribution is defined entirely by its degrees of freedom (df). Unlike the symmetric normal distribution, it is right-skewed and only takes non-negative values, which is why it's used to test whether observed categorical data matches an expected distribution.

Variables:
df:Degrees of freedom — typically (categories − 1) for goodness-of-fit tests
χ²:The chi-square test statistic being evaluated
How It Works (Step-by-Step)
  • 1Enter the degrees of freedom (df) for the test and the observed chi-square statistic (χ²).
  • 2View the distribution's mean and variance to understand where your statistic falls relative to the expected spread.
Real-World Numerical Example
A Goodness-of-Fit Test with 5 Degrees of Freedom

A categorical goodness-of-fit test has 5 degrees of freedom and produces a chi-square statistic of 11.07 (the standard 5% critical value for df=5).

Mean = df = 5.
Variance = 2 × df = 2 × 5 = 10.
Result: The distribution has a mean of 5 and a variance of 10; a χ² statistic of 11.07 or higher occurs by chance only 5% of the time at this degrees of freedom.
Calculation Best Practices & Tips
In medical screening and classification, prior prevalence dramatically affects posterior positive predictive values (Bayes’ Theorem).
Use Bessel’s correction (n - 1) when calculating sample standard deviation to avoid systematically underestimating population variance.
Check assumptions of normality and homoscedasticity before making inferences with linear regression and ANOVA.

Frequently Asked Questions (FAQ)

It tests whether observed categorical frequencies significantly differ from expected theoretical frequencies — common uses include goodness-of-fit tests and tests of independence between two categorical variables in a contingency table.

As degrees of freedom increase, the chi-square distribution becomes less skewed and starts to resemble a normal distribution — both its mean (= df) and variance (= 2×df) grow directly with degrees of freedom.

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