If I take a sample, I don't always get the same results. However, sampling distributions—ways to show every possible result if you're taking a sample—help us to identify the different results we can get …

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Introduction to sampling distributions Notice Sal said the sampling is done with replacement. This means during the process of sampling, once the first ball is picked from the population it is replaced back …

In a significance test about a population mean, we first calculate a test statistic based on our sample results. We can then use a table to estimate the p-value based on that test statistic using a t …

In shape, the sampling dist is approximately normal, but it's a discrete random variable distribution, with the tops of the rectangles representing the relative frequency of each sample proportion (not the area …

The Central LImit theorem assumes that for a sampling distribution of sample means/proportions when n > 30, the distribution will appear basically normal. And by the way, the distribution has to be normal …

The sampling distribution (of sample proportions) is a discrete distribution, and on a graph, the tops of the rectangles represent the probability. The z-table/normal calculations gives us information on the …

A sampling distribution shows every possible result a statistic can take in every possible sample from a population and how often each result happens - and can help us use samples to make predictions …

What is the difference between standard deviation of sampling distribution, and unbiased standard deviation of a sample? The formula for both are different. And which one is a better/closer estimate …