Chi-squared proportion test is an inferential technique to assess two competing hypotheses about the population proportions across k groups. Specifically, the chi-square test assesses whether the k population proportions are equal. If there is significant evidence that the population proportions are different, we want to conduct post hoc testing to investigate where the proportions are different.

Chi-squared proportion test evaluates the equality of k population proportions based on observed data assuming that the observations are representative of the population of interest and independent. Further, the data must be meet certain sample size conditions to be appropriate.

Chi-squared proportion test compares the observed data with what we expect under the null hypothesis: all group proportions are equal. The resulting p-value tells us how likely it is to observe the evidence we have for the alternative hypothesis or more when the null hypothesis is true. If the p-value is less than the specified significance level (e.g., less than 0.05), we reject the null hypothesis in favor of the alternate hypothesis. In that case, chi-square's proportion Test indicates that we have significant evidence that at least one population proportion is different. Otherwise, we fail to reject the null hypothesis, indicating we do not have significant evidence that at least one population proportion is different.

If Chi-squared proportion test result is significant, we can conduct post hoc tests to investigate which proportions differ. The post hoc test for the chi-squared proportion test is pairwise chi-squared proportion tests, and we compute pairwise confidence intervals. We correct for multiple simultaneous inferences by applying a Bonferroni or Benjamini-Hochberg correction.

Step 1: To use this app, go to the Dataset and Hypothesis Tab and upload your .csv type dataset.

Step 2: You can check the assumptions provided in the 'Summary & Assumptions Check' tab. .

Step 3: You can check the result of the chi-squared test (test statistics, decision making, and test visualization) in the 'Hypothesis Test' tab.

Step 3 (Optional): If the hypothesis test produces a significant result, you can view the results of the appropriate post hoc procedures in the 'Post Hoc' tab.

Please contact us if you have any questions at datascience@colgate.edu.

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