Statistics in A-Level Biology: chi-squared, t-test and correlation

Choosing the right test

• Chi-squared: comparing observed frequencies with expected frequencies in categories (e.g. genetic cross ratios).
• Student's t-test: comparing the means of two sets of continuous data (e.g. leaf length in sun vs shade).
• Correlation coefficient: testing for an association between two continuous variables measured on the same individuals.

Writing the null hypothesis

The null hypothesis always states there is no difference, no association, or no significant difference between observed and expected. For a genetic cross: 'There is no significant difference between the observed and expected numbers of offspring in each phenotype.'

Degrees of freedom and critical values

For chi-squared, degrees of freedom = number of categories − 1. For an unpaired t-test, it is (n₁ + n₂) − 2. Look up the critical value at p = 0.05. If your calculated value is greater than the critical value, reject the null hypothesis: the difference is significant and there is less than a 5% probability it is due to chance.

Interpretation language

• Never say the test 'proves' anything — say results are or are not significant.
• Correlation does not show causation; other variables may be responsible.
• Always relate your conclusion back to the biological context in the question.