Final answer:
To assess the significance of the difference between observed and Hardy-Weinberg expected genotype frequencies, scientists use a chi-square test. By comparing the calculated chi-square value with a critical value from the distribution table, researchers determine if the population is deviating from the Hardy-Weinberg equilibrium.
Step-by-step explanation:
To determine whether the difference between actual genotype frequencies and those expected under Hardy-Weinberg equilibrium (HWE) is significant, scientists often perform a χ² (chi-square) test. This statistical test compares the observed genotype frequencies with the expected frequencies as calculated by the HWE principle.
The Hardy-Weinberg principle states that a population's allele frequencies will remain constant from generation to generation, given that certain conditions (no mutations, no gene flow, random mating, no genetic drift, and no selection) are met. The principle is mathematically represented by the equation p² + 2pq + q² = 1, where 'p' and 'q' are the frequencies of the dominant and recessive alleles, respectively.
To carry out a chi-square test, first calculate the expected genotype frequencies using the HWE formula. Then, use the chi-square formula χ² = Σ((observed - expected)² / expected) to determine the chi-square value, which is then compared against a critical value from the chi-square distribution table corresponding to the degrees of freedom and desired confidence level (usually 95%).
Degrees of freedom are generally calculated as the number of distinct genotypes minus one. If the computed chi-square value is greater than the critical value, the null hypothesis that the population is in HWE is rejected, indicating that evolutionary forces may be acting on the population.