Description: The G test is a statistical test used to determine if there are significant differences between observed and expected frequencies in categorical data. This test is based on comparing the frequencies of different categories in a dataset, allowing researchers to assess whether the observed differences are the result of random variations or indicate a real trend. The G test is commonly used in the analysis of contingency tables, where the relationships between two or more categorical variables are examined. Unlike other tests, such as the chi-squared test, the G test is based on the likelihood function, making it particularly useful in situations where data are expected to follow a multinomial distribution. The interpretation of G test results is done through the p-value, which indicates the probability of observing the data if the null hypothesis is true. A low p-value suggests that there is sufficient evidence to reject the null hypothesis, implying that the observed differences are significant. In summary, the G test is a valuable tool in applied statistics, providing a robust method for analyzing categorical data and assessing the significance of observed differences between groups.
