The significance of difference between propoions can also be tested by-
**Core Concept**
The significance of difference between proportions is a statistical concept used to determine whether two or more independent samples have a significant difference in their population proportions. This is essential in medical research to compare the efficacy of treatments, prevalence of diseases, or outcomes between different groups.
**Why the Correct Answer is Right**
The correct test for the significance of difference between proportions is the Chi-Square test. This test is used when the data is categorical and the sample sizes are sufficiently large. The Chi-Square test calculates the probability of observing the difference between the observed and expected frequencies, assuming that there is no real difference between the proportions. It is a widely used statistical test in medical research, particularly in studies involving case-control designs or cohort studies.
**Why Each Wrong Option is Incorrect**
**Option A:** This option is incorrect because the t-test is used to compare the means of two independent groups, not proportions. While the t-test can be used to compare proportions, it is not the most appropriate test for this purpose.
**Option B:** This option is incorrect because the Fisher Exact test is used to compare two independent samples when the sample sizes are small. While it can be used to compare proportions, it is not the most widely used or recommended test for this purpose.
**Option C:** This option is incorrect because the ANOVA test is used to compare the means of three or more independent groups, not proportions. While ANOVA can be used to compare proportions, it is not the most appropriate test for this purpose.
**Clinical Pearl / High-Yield Fact**
When comparing proportions, it's essential to check for normality of the data and to use the correct statistical test. The Chi-Square test is generally preferred when the sample sizes are sufficiently large, while the Fisher Exact test is preferred when the sample sizes are small.
**Correct Answer: C. ANOVA test**