The chi-squared (χ²) test is used in genetics to determine whether observed genetic ratios significantly differ from expected ratios, and whether this difference is due to chance or another factor.

The formula will be provided, but it is:

χ2=∑E(O−E)2​

Where O is the observed frequency and E is the expected frequency.

1. Large sample size
2. Discrete data categories (like yes or no, heads or tails, red or blue)
3. Using raw counts (not percentages or rates)
4. A comparison of experimental and theoretical results

The null hypothesis assumes that there is no significant difference between observed and expected results, and that any difference is due to chance alone.

We accept the null hypothesis when the χ² value is lower than the critical value at a 5% significance level (p = 0.05).

This suggests that the differences between the observed and expected frequencies are due to chance.

A significant result in a χ² test is a χ² value greater than the critical value at a 5% significance level (p = 0.05).

We reject the null hypothesis when the χ² value is greater than the critical value at a 5% significance level (p = 0.05).

This suggests that there is a significant difference between the observed and expected results, and that the difference is due to a factor other than chance.

More observations reduce the relative effect of chance on the difference between expected and observed results.

df = n − 1, where n is the number of categories, classes, or outcomes in the analysis.

The critical value increases with more degrees of freedom.