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Stating the Null and Alternative Hypotheses
The null hypothesis in a chi-square goodness-of-fit test states that the sample of observed frequencies supports the claim about the expected frequencies. The alternative hypothesisstates that there is no support for the claim pertaining to the expected frequencies. This deviates from our normal approach to place our expected (preferred) outcome in the alternative hypothesis. Just be aware of this.
For example:
Ho: Alleles segregate equally.
HA: Alleles do not segregate equally.
Observed and Expected Frequencies
Observed frequencies are the number of actual observations noted for each category of a frequency distribution with chi-squared analysis. Expected frequencies are the number of observations that would be expected for each category of a frequency distribution assuming the null hypothesis is true with chi-squared analysis.
Calculating the Chi-Square Statistic
Note k, which is equal to the number of categories in the study. In the above example, k equals the number of different phenotypes (2).
Determining the Critical Chi-Square Score (Two Approaches)
The critical chi-square score is a function of the degrees of freedom, which for the chi-square goodness-of-fit test is equal to k-1. Note that the chi-square probability distribution is influenced a great deal by the value of k.