The Chi-Squared Test |

The Chi-squared test is a statistical test used to see if the results of an experiment support the expected result.
- The test tells you if you can reject the ‘null hypothesis’ – which is that there is no significant difference between the expected data and observed data
- If you reject the null hypothesis, it means that there is a significant difference between the expected and observed data.
You can use the chi-squared test to compare the goodness of fit of observed phenotypic ratios with expected ratios.
To run the chi-squared test, you need to calculate a test statistic and then compare it with a critical value in a table.

Use this formula to work out the test statistic:
Find the right critical value from the chi-squared table:
1. Work out the degrees of freedom (number of classes – 1) and the probability level (p-value) you are using (usually 0.05)
2. Look in the corresponding row and column of the table to find the critical value
Compare the critical value and the test statistic
1. If the test statistic is > or = to the critical value, there is a significant difference between the observed and the expected results, so the null hypothesis can be rejected
2. If the test statistic is < the critical value, there is no significant difference and the null hypothesis cannot be rejected