Compares observed and expected counts, for example testing Mendelian ratios.
χ² = Σ (O-E)²/E; compare to a critical value with degrees of freedom = categories - 1. Used for ratios such as 9:3:3:1. If χ² is above the critical value at p = 0.05, reject the null hypothesis.
Use counts, not percentages. Expected frequencies should be at least 5. Degrees of freedom equal categories minus one.
Key idea: it tests whether observed deviations from expectation are bigger than chance.
Compares observed and expected counts, for example testing Mendelian ratios.
Chi-Squared Test is one of the 51 topics in Lab Methods & Biostatistics. It is rated Intermediate, so it suits learners who already know the core vocabulary. Related topics in this area include ANOVA, Null Hypothesis, P-Values and Multiple Testing.
Testing whether fly offspring match a 3:1 ratio.
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to Chi-Squared Test. Intermediate topics assume the Beginner vocabulary.
The closest topics to Chi-Squared Test are ANOVA, Null Hypothesis, P-Values and Multiple Testing, Phylogenetic Statistics. Comparing them side by side is a good way to see how the ideas connect.
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Chi-Squared Test. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
Chi-Squared Test: Compares observed and expected counts, for example testing Mendelian ratios. ANOVA: Compares means across three or more groups by partitioning variance. Read both side by side and note what each one explains.
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with ANOVA and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.
Compares means across three or more groups by partitioning variance.
📊 Lab Methods & BiostatisticsThe default assumption of no effect or difference, which data may lead us to reject.
📊 Lab Methods & BiostatisticsP-values give the probability of data under the null; corrections like Bonferroni control false positives across many tests.
📊 Lab Methods & BiostatisticsBootstrap and posterior probabilities measure confidence in the branches of evolutionary trees.
📊 Lab Methods & Biostatistics