Why This Calculator Matters
Hardy Weinberg analysis checks whether genotype counts fit a stable population model. The model assumes random mating, no selection, no mutation, no migration, and a very large population. Real samples often break one or more assumptions. A chi square test gives a clear way to measure that departure. This calculator turns raw genotype counts into allele frequencies, expected counts, test components, and an interpretation.
What The Test Measures
The calculator estimates p and q from the observed AA, Aa, and aa classes. It then predicts the genotype counts expected under equilibrium. The expected AA count is p squared times the sample size. The expected Aa count is two times p times q times the sample size. The expected aa count is q squared times the sample size. Each observed class is compared with its expected class.
Reading The Result
The chi square statistic grows when observed and expected counts are far apart. A small statistic suggests the sample is close to equilibrium. A large statistic suggests a stronger deviation. For a standard biallelic Hardy Weinberg test, the calculator uses one degree of freedom because one allele frequency is estimated from the same sample. At the five percent level, a statistic above 3.841 usually rejects equilibrium.
Practical Notes
Use exact genotype counts, not percentages. The total sample size should represent one population, one locus, and one sampling period. Avoid mixing different subgroups unless that is the research question. If expected counts are very small, the chi square approximation can be weak. Larger samples usually give more reliable decisions.
Best Uses
This tool helps biology students, genetics instructors, field researchers, and data analysts document a repeatable calculation. The export buttons make it easier to save a report for lab notes. The example table also shows how each genotype contributes to the final statistic, so the decision is easier to audit.
Good Reporting Habits
Report the sample size, allele frequencies, expected counts, chi square value, chosen alpha level, and final decision. Keep the genotype labels consistent across tables. When publishing or submitting work, also describe the sampling method. Clear reporting helps readers see whether the statistical decision matches the biological question. It also supports later review and sharing.