Central Limit Theorem: A sampling distribution of the mean is approximately normally distributed if the sample size is sufficiently large. This is true no matter what the population distribution is.

Example 1: The population from which samples are selected is {1,2,3,4,5,6}. This population has a mean of 3.5 and a standard deviation of 1.70783. The next display shows a histogram of the population.

Describe the abstract idea of a sampling distribution and how it reflects the sample to sample variability of a sample statistic or point estimate. Identify the ...

When an experiment is reproduced we almost never obtain exactly the same results. Instead, repeated measurements span a range of values because of biological variability and precision limits of ...

The normal distribution (also known as the Gaussian distribution) is arguably the most important distribution in Statistics. It is often used to represent continuous random variables occurring in ...

This paper presents the results of a limited investigation which brings into focus the difficulties encountered in developing exact distribution-free methods for stratified simple random sampling. It ...

Stratified sampling is considered, where (a) the mean integrated squared error (MISE) metric is used in place of the mean squared error (MSE) metric; (b) the entire ...

J.B. Maverick is an active trader, commodity futures broker, and stock market analyst 17+ years of experience, in addition to 10+ years of experience as a finance writer and book editor. T-tests are ...