Ch. 8 & 9

Cluster Sampling

a method often used to lower the cost of sampling if the population is dispersed over a wide geographic area. the area is divided into smaller units (counties, precincts, blocks, etc.) called primary units. Then a few primary units are chosen, and a random sample is selected from each primary unit.

Stratified Random Sampling

a population is first divided into subgroups called strata. A sample is the chosen from each stratum. If, for example, the population of interest consisted of all undergraduate students, the sample design might call for sampling 62 freshmen, 51 sophomores, 40 juniors, and 39 seniors.

Bias

a possible consequence if certain members of the population are denied the chance to be selected for the sample. As a result, the sample may not be representative of the population

Sampling distribution of the sample mean

a probability distribution consisting of all possible means of samples of a given size slected from the population

Probability Sample

a sample of items or individuals chosen so that each member of the population has a chance of being included in the sample

Simple Random Sampling

a sampling scheme such that each member of the population has the same chance of being selected as part of the sample.

Point Estimate

a single value computed from a sample and used to estimate a population parameter. Example; if the sample mean is 1020, it is the best estimate of the population mean.

Systematic random Sampling

assuming the population is arranged in some way, such as alphabetically, by height, or in fa file drawer, a random starting point is selected, then every kth item becomes a member of the sample. if a sample design called for interviewing everything ninth household on Main street starting with 932 Main, the sample would consist of households at 932 Main, 941 Main, 950 Main, and so on.

Sampling Error

the difference between a sample statistic and the corresponding population parameter. Example: The sample mean income is $22,100; the population mean is $22,000. The sample error is $22,100 - $22,000 = $100. This error can be attributed to sampling ---that is, chance.

Interval Estimate

the interval within which a population parameter probably lies, based on sample information. Example: according to sample data, the population mean is n the interval between 1.9 and 2.0 pounds

Central Limit Theorem

the sampling distribution of the sample mean will approach a normal distribution regardless of the shape of the population as sample size increases

Finite-population correction factor (FPC)

when sampling without replacement from a finite population, a correction term is used to reduce the standard error of the mean according to the relative size of the sample to the size of the population. the correction factor is used when the sample is more than 5 percent of a finite population.