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How does sample size n affect the shape of a sampling distribution?
As sample sizes increase, the sampling distributions approach a normal distribution. As the sample sizes increase, the variability of each sampling distribution decreases so that they become increasingly more leptokurtic. The range of the sampling distribution is smaller than the range of the original population.
How do sample and population distributions differ from each other?
The population distribution gives the values of the variable for all the individuals in the population. The sampling distribution shows the statistic values from all the possible samples of the same size from the population. It is a distribution of the statistic.
How is the sampling distribution of the sample proportion calculated?
The Sampling Distribution of the Sample Proportion If repeated random samples of a given size n are taken from a population of values for a categorical variable, where the proportion in the category of interest is p, then the mean of all sample proportions (p-hat) is the population proportion (p).
When does the sample proportion have a mean of P?
If the population has a proportion of p, then random samples of the same size drawn from the population will have sample proportions close to p. More specifically, the distribution of sample proportions will have a mean of p. We also observed that for this situation, the sample proportions are approximately normal.
How is the sample proportion of a population approximated?
The true proportion in the population is equal to some unknown value p̂. The sampling distribution of p̂ can be approximated by a normal distribution with distribution Even though we do not know the exact value of p, we can use p̂ as a good estimator of of p, to approximate the distribution of the sample proportion of p̂
How big of a sample is too big for a normal distribution?
Well, it really depends on the population distribution, as we saw in the simulation. The general rule of thumb is that samples of size 30 or greater will have a fairly normal distribution regardless of the shape of the distribution of the variable in the population.