BSTAT

Estimate

A particular value of the estimator.

Population

Consists of all items of interest in a statistical problem.

Central Limit Theorem

For any population X with the expected value mu and standard deviation theta, the sampling distribution X will be approximately normal if the sample size n is sufficiently large.

Cluster Sampling

The population is first divided up into mutually exclusive and collectively exhaustive groups called clusters. It also includes observations from randomly selected clusters.

Stratified Random Sampling

The population is first divided up into mutually exclusive and collectively exhaustive groups, called strata.

Bias

The tendency of a sample statistic to systematically over or underestimate a population parameter. It is often caused by samples that are not representative of the population.

Expected Value

the value of X is the same as the expected value of the individual observation. E(X) = u (The sample mean is an unbiased estimator of the population mean.)

Simple Random Sample

A sample of x observations that has the same probability of being selected from the population as any other sample of n observations.

Non-response Bias

A systematic difference in preferences between respondents and non-respondents to a survey or a poll.

Selection Bias

A systematic under-representation of certain groups from consideration for the sample.

Sample Statistic

Inference about the unknown population parameter

Parameter

Is a constant although its value maybe unknown

Sample

Is a subset of the population.

The Standard Error of the Sample Mean

Standard deviation of the sample mean X is referred to as the standard error of the sample mean

Stratified vs Cluster Sampling

Stratified Sampling, the sample consists of observations from each group. Cluster Sampling, the sample consists of observation.

Estimator

When a statistic is used to estimate a parameter

Statistic

is a random variable whose value depeneds on the chosen random sample.