Chi Square For Genetics Calculator

Work out chi square for genetics instantly with clear inputs, formula shown and shareable results.

About Chi Square For Genetics Calculator

Computes the chi-square goodness-of-fit statistic against a Mendelian ratio and compares it with the 3.841 critical value at one degree of freedom.

Formula

Chi-square test

chi2 = sum of (observed - expected)^2 / expected; with two classes there is 1 degree of freedom and a 5% critical value of 3.841

Worked example

With the default values below, Chi Square For Genetics Calculator returns Chi-square statistic of 1.3333. Change any input above and every figure here is recalculated in your browser.

Inputs used

Observed in the first classaccepts 0–1,000,000: 290
Observed in the second classaccepts 0–1,000,000: 110
Expected ratio part for the first classaccepts 1–20: 3
Expected ratio part for the second classaccepts 1–20: 1

Results

Chi-square statistic: 1.3333
Expected count in the first class: 300.00
Expected count in the second class: 100.00
Test result at p = 0.05: Does not exceed the 3.841 critical value — consistent with the expected ratio

Step by step

1Values used
2Chi-square test
3Chi-square statistic= 1.3333
4Expected count in the first class= 300.00
5Expected count in the second class= 100.00
6Test result at p = 0.05= Does not exceed the 3.841 critical value — consistent with the expected ratio

Assumptions and limits

The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

What does a large chi-square mean?

The data depart from the expected ratio more than sampling error alone would explain, so the genetic model is questioned.

When is the test invalid?

When any expected count falls below about 5, where an exact test should be used instead.