Ch. 7: Probability and Samples: The Distribution of Sample Means

Sampling error

The natural discrepancy, or amount of error, between a sample statistic and its corresponding population parameter

Distribution of sample means

The collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a population

Sampling distribution

Is a distribution of statistics obtained by selecting all the possible samples of a specific size from a population

Central limit theorem

For any population with mean (mu) and standard deviation o-, the distribution of sample means for sample size n, will have a mean of (mu) and a standard deviation of o-/sqrt(n) and will approach a normal distribution as n approaches infinity

Expected value of M

The mean of the distribution of sample means is equal to the mean of the population of scores, (mu), and is called the expected value of M

Standard error of M

The standard deviation of the distribution of sample means, O-M, is called the standard error of M. The standard error of M. The standard error provides a measure of how much distance is expected on average between a sample mean (M) and the population mean (mu)

Law of large numbers

States that the larger the sample size (n), the more probable it is that the sample mean will be close to the population mean