![]() ![]() Or not too close to one, then we can say that, well, look, this sampling distribution is That the true proportion isn't too close to zero Person we're asking, that it's approximately independent. Because our sample size is so much smaller than the population, it's way less than 10%, we can assume that each Proportions we could get and their likelihoods with And so we can describe the possible sample And it's going, thisĭistribution's going to be specific to what our sample size is, for n is equal to 100. Sampling distribution of the sample proportions, of the sample proportions, proportions. We've talked about it when we thought about The tools in statistics to think about this, the distribution of the possible sample proportions we could get. And just to appreciate that we're not always going to get 0.54, there could've been a situation where we sampled a different 100, and we would've maybe gottenĪ different sample proportion. So out of the 100, let's say that 54 say that they're going And we calculate the sample proportion that support candidate A. So this sample size, let's say n equals 100. Statistic from that sample in order to estimate this parameter. So instead, we do the thing that we tend to do in statistics is, is that we sample this population, and we calculate a Will not be realistic to ask, well, all 100,000 people. ![]() Is the proportion that support, support candidate A. The population proportion, which would be, this The them, who do you support? And from that, we would be able to get To the entire population of likely voters right over here, let's say there's 100,000 likely voters, and we would ask every one of In figuring out, well, what's the likelihood thatĬandidate A wins this election? Well, ideally, we would go It is election season, and there is a runoff between candidate A versus candidate B.
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