What statistical method is used to determine the goodness of fit in observed data?

What statistical method is used to determine the goodness of fit in observed data?

• A. T-test
• B. Anova test
• C. Chi-squared test
• D. Regression analysis

Answer: (C)

The Chi-squared test is the statistical method commonly used to assess the goodness of fit between observed data and expected data under a specific hypothesis. This test evaluates how well the observed categorical data matches the expected distribution. The key aspect of the Chi-squared test is that it compares the frequencies of different categories to see if they align with what would be expected if the null hypothesis were true. In practice, researchers calculate the Chi-squared statistic by summing the squared differences between observed and expected frequencies, divided by the expected frequencies. A higher Chi-squared value indicates a greater disparity between the observed and expected values. If the calculated p-value is below a certain significance level (often 0.05), it suggests that the observed data significantly deviates from the expected data, indicating that the null hypothesis may not hold true. This method is particularly useful in genetics for tasks such as determining whether allele frequencies in a population conform to Hardy-Weinberg equilibrium or if there is a significant association between categorical variables, such as the presence or absence of a trait in different groups.

The Chi-squared test is the statistical method commonly used to assess the goodness of fit between observed data and expected data under a specific hypothesis. This test evaluates how well the observed categorical data matches the expected distribution. The key aspect of the Chi-squared test is that it compares the frequencies of different categories to see if they align with what would be expected if the null hypothesis were true.

In practice, researchers calculate the Chi-squared statistic by summing the squared differences between observed and expected frequencies, divided by the expected frequencies. A higher Chi-squared value indicates a greater disparity between the observed and expected values. If the calculated p-value is below a certain significance level (often 0.05), it suggests that the observed data significantly deviates from the expected data, indicating that the null hypothesis may not hold true.

This method is particularly useful in genetics for tasks such as determining whether allele frequencies in a population conform to Hardy-Weinberg equilibrium or if there is a significant association between categorical variables, such as the presence or absence of a trait in different groups.