 Hello, this is a video on two sample hypothesis testing, comparing two proportions. You wish to test the following claim, H1, or the alternative hypothesis, at a significant level of alpha equals 0.005. I have my two hypotheses listed here, the null hypothesis H0 and the alternative hypothesis H1. Notice that H1 has not equal to as its sign. All right, and that's also our claim. You obtain 408 successes in a sample size of 716 from the first population. You obtain 304 successes in a sample size of 498 from the second population. So I'm going to perform the hypothesis test here. I'll collect the test statistic, the p-value, and then I'll make a judgment call based on what I should do, whether I should reject the null hypothesis or fail to reject it. So all the calculations are going to be done in Google Sheets. First thing you want to do is you want to list all of your sample data values. For instance, in this case, group one. Group one's always going to be the group on the left. Group two is always going to be the group on the right. So group one, the number of successes is 408. And the sample size is 716. Group two, the number of successes is 304. And the sample size is 498. You need that information as well as the sign of the alternative hypothesis in order to get your answer in Google Sheets. So let's do this now. So you're going to go to Google Sheets. Once you get to Google Sheets, you're going to go to the data list tab. So you'll go to the data list tab first. And you're dealing with two samples with proportions. So you'll go over to the area that says two prop CL p-value. That's where you do your two sample hypothesis testing when it comes to proportions. Type in your number of successes in the first sample. Which would happen to be in this case, 408. Your sample size, 716. In your second group, you had 304 successes out of 498. And the sign of your alternative hypothesis was not equal to. So you have all this information here, but all you really care about when you do a hypothesis test would be the test statistic and the p-value. So the test statistic two decimal places is negative 1.41. The p-value is about 0.1576. So let's put those values here. The test statistic was negative 1.41. And the p-value would be 0.1576. So now using the p-value, let's compare it to our value alpha. So remember, our claim was the alternative hypothesis here. Our p-value was 0.1576. That's the p-value. And our alpha value was 0.005. They gave this to us in the question. Since the p-value is greater than alpha, we fail to reject the null hypothesis. Fail to reject the null hypothesis because the p-value is greater than alpha. So look at your summary statements here for hypothesis testing. We failed to reject H naught and the original claim does not include equality. So we form our statement in the form shown here. And this tells us that there is not sufficient evidence to support the claim that the first population proportion is not equal to the second population proportion. And that's an overview of how to run a hypothesis test when comparing to population proportions. Thank you for watching.