Two Sample Proportion Test Concepts

This video tutorial covers the two sample proportion test, explaining its purpose, assumptions, and hypotheses. It details the calculation of the test statistic and how to conduct the test using Minitab. The video also discusses the importance of Fisher's exact test for accuracy in small sample sizes. A practical example involving survey response rates is provided to illustrate the application of the test.

What is the primary purpose of a two sample proportion test?

• To test for correlation between two variables

• To analyze variance within a single group

• To determine if the proportions of two groups differ

• To compare means of two groups

Which of the following is an assumption for the two sample proportion test?

• The sample must be continuous

• The sample size must be less than 30

• The sample must be unbiased and representative

• The sample must be normally distributed

What does the term 'binomial distribution' refer to in the context of the test?

• A distribution of continuous data

• A distribution of discrete data with two possible outcomes

• A distribution of categorical data

• A distribution of ordinal data

What is the null hypothesis in a two sample proportion test?

• The difference between population proportions is zero

• The difference between sample means is zero

• The variance of two samples is equal

• The correlation between two variables is zero

What is the hypothesized difference in the null hypothesis denoted by?

• Delta 0

• P2 hat

• Z calculated

• P1 hat

What does the test statistic 'Z calculated' represent in the two sample proportion test?

• The difference between sample means

• The standardized difference between sample proportions

• The correlation coefficient

• The ratio of sample variances

What is the role of 'P1 hat' and 'P2 hat' in the test statistic formula?

• They are the hypothesized proportions

• They represent the sample means

• They are the observed proportions of events in the samples

• They indicate the sample variances

What does 'N1' and 'N2' represent in the context of the test?

• The number of events in the samples

• The number of trials in the samples

• The sample means

• The sample variances

What is the role of 'X1' and 'X2' in the test statistic formula?

• They are the number of events in the samples

• They represent the sample means

• They indicate the sample variances

• They are the hypothesized proportions

When do we reject the null hypothesis in a two sample proportion test?

• When the test statistic is less than the critical value

• When the test statistic is equal to the critical value

• When the absolute value of the test statistic is greater than the critical value

• When the p-value is greater than the significance level