Joon samples 100 first-time brides and 53 reply that they are younger than their grooms. She performs a hypothesis test to determine if the percentage is the same or different from 50. So how is the tops of all the rects below 0.7 summed up equal to the area of the rectangles (area under the normal curve) that is below 0. Joon believes that 50 of first-time brides in the United States are younger than their grooms. test since we are specifically testing to see if the proportion is greater than 0.5. The probability that sample proportion < 0.7 is the tops of all the rectangles below 0.7 summed up for the sampling distribution.īut, for the normal dist (density curve) that approximates our sampling dist, using normalcdf on a calculator or a z-table gives us the proportion of the area under the curve that is < 0.7. Limit Theorem, using the sample proportion as an estimate of the. We know that the dist is approximately normal, and we have it's mean, and SD. So we have a sampling dist, and we want to find the probability that we get a sample proportion that is less than 0.7. If you look at the table above, you see that some bars have less than 20 grams. The z-table/normal calculations gives us information on the area underneath the normal curve, since normal dists are continuous. The one-sample t-test is a statistical hypothesis test used to determine. The sampling distribution (of sample proportions) is a discrete distribution, and on a graph, the tops of the rectangles represent the probability. I'm still confused as to how we can use normal calculations, like a z-table.
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