STAT 301 Lecture Notes - Point Estimation, Sampling Distribution, Standard Deviation
Document Summary
How to approximate a sampling distribution for p. Randomly select a sample of size n from the population. Repeat steps 2 and 3 1000 times. Plot a histogram of the p values to see what the sampling distribution looks like. If it is large and is not extreme (i. e. , is not close to 0 or 1), then the distribution of p is approximately normal. Rule: normal if both n 10 and (1- )n 10. Say we want to find p(p < . 34) We can find this since p~n( p, 2p) when rule holds. So we standardize p using a z-score where p p. The standard error is an estimate of the standard deviation. The standard error for p, denoted se(p) is. The estimates we get from the samples are incorrect. We can only hope that the estimates are close to the true values. Standard errors help us determine a range of possible values that may contain the true value.
