Hardy Weinberg Chi Square Testing Guide
Hardy Weinberg analysis checks whether genotype counts match equilibrium expectations. The method is useful in population genetics and breeding studies. It compares observed genotypes with expected genotypes based on allele frequencies. Large differences may suggest selection, migration, mutation, assortative mating, small samples, or counting errors.
What The Calculator Measures
This calculator estimates allele frequencies from observed AA, Aa, and aa counts. It can also use a known allele frequency when supplied. The expected values are then computed with p squared, two p q, and q squared. Each observed value is compared with its expected value. The chi square statistic combines those differences into one test value.
Why Expected Counts Matter
Expected counts should not be too small. Low expected values can make the chi square approximation weak. Many textbooks prefer every expected genotype count to be at least five. When values are lower, you should treat the decision carefully. More samples or an exact test may be better.
Understanding The Decision
The p value shows how unusual the observed deviation is under equilibrium. A small p value means the sample is unlikely under the model. If the p value is below alpha, the calculator marks the result as significant. This does not prove a single biological cause. It only shows evidence against equilibrium.
Good Data Practices
Use counts from one population and one generation. Avoid mixing locations, cohorts, or sampling methods. Confirm that genotype classes are mutually exclusive. Round only for reporting, not before calculation. Review the expected table before interpreting the final decision.
Practical Use Cases
Researchers can screen loci for equilibrium before association studies. Teachers can demonstrate allele frequency estimation. Breeders can compare observed mating outcomes with predicted proportions. Students can export results for lab reports and assignments.
Interpreting With Care
Hardy Weinberg testing is a model check, not a complete explanation. A significant result needs biological context. Nonrandom mating, hidden population structure, genotyping mistakes, and natural selection can all affect the outcome. A nonsignificant result also needs care. It may reflect equilibrium, or it may reflect low statistical power.
The calculator shows allele frequencies, expected counts, component values, degrees of freedom, p value, and conclusion. Use downloads to save your work.