- The Chi-squared test is a statistical test used to see if the results of an experiment support the expected result.
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- 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:
- Work out the degrees of freedom (number of classes – 1) and the probability level (p-value) you are using (usually 0.05)
- Look in the corresponding row and column of the table to find the critical value
- Compare the critical value and the test statistic
- 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
- If the test statistic is < the critical value, there is no significant difference and the null hypothesis cannot be rejected