Chapter 10

Systematic Sample

A sample drawn by selecting individuals systematically from a sampling frame. When there is no relationship between the order of the sampling frame and the variables of interest, a systematic sample can be representative.

Representative

A sample is said to be representative if the statistics computed from it accurately reflect the corresponding population parameters.

Census

A sample that consists of the entire population is called a census.

Cluster Sample

A sampling design in which entire groups, or clusters, are chosen at random. Cluster sampling is usually selected as a matter of convenience, practicality, or cost. Each cluster should be representative of the population, so all the clusters should be internally varied and similar to each other.

Stratified Random Sample

A sampling design in which the population is divided into several subpopulations, or strata, and random samples are then drawn from each stratum. If the strata are internally similar, but are different from each other, a stratified sample may yield more consistent results than an SRS.

Undercoverage

A sampling scheme that biases the sample in a way that gives a part of the population less representation than it has in the population.

Pilot

A small trial run of a survey to check whether questions are clear. A pilot study can reduce errors due to ambiguous questions.

Sample Survey

A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population. Polls taken to assess voter preferences are common sample surveys.

Sample

A (representative) subset of a population, examined in hope of learning about the population.

Convenience Sample

A convenience sample consists of the individuals who are conveniently available. Convenience samples often fail to be representative because every individual in the population is not equally convenient to sample.

Sampling Frame

A list of individuals from whom the sample is drawn.

Population Parameter

A numerical characteristic of a model for a population. We rarely expect to know the true value of a population parameter, but we do hope to estimate it from sam-pled data. For example, the mean income of all employed people in the country is a population parameter.

Bias

Any systematic failure of a sampling method to represent its population is bias. Biased sampling methods tend to over- or underestimate parameters. It is almost impossible to recover from bias, so efforts to avoid it are well spent. Common errors include relying on voluntary response, under coverage of the population, nonresponse bias, and response bias.

Response Bias

Anything in a survey design that influences responses falls under the heading of response bias. One typical response bias arises from the wording of questions, which may suggest a favored response. Voters, for example, are more likely to ex-press support of "the president" than support of the particular person holding that office at the moment.

Voluntary Response Bias

Bias introduced to a sample when individuals can choose on their own whether to participate in the sample. Samples based on voluntary response are always invalid and cannot be recovered, no matter how large the sample size.

Nonresponse Bias

Bias introduced when a large fraction of those sampled fails to respond. Those who do respond are likely to not represent the entire population. Voluntary response bias is a form of nonresponse bias, but nonresponse may occur for other reasons. For example, those who are at work during the day won't respond to a telephone survey conducted only during working hours.

Simple Random Sample (SRS)

In a simple random sample each subset of the population has an equal chance of selection.

Randomization

The best defense against bias is randomization, in which each individual is given a fair, random chance of selection.

Multistage Sample

Sampling schemes that combine several sampling methods are called multistage samples. For example, a national polling service may stratify the country by geo-graphical regions, select a random sample of cities from each region, and then interview a cluster of residents in each city.

Sample Statistic

Statistics are values calculated for sampled data and are used to estimate a population parameter. For example, the mean income of all employed people in a representative sample can provide a good estimate of the corresponding population parameter. The term "sample statistic" is sometimes used, usually to parallel the corresponding term "population parameter."

Population

The entire group of individuals or instances about whom we hope to learn.

Sampling Variability

The natural tendency of randomly drawn samples to differ, one from another. Sometimes called sampling error, sampling variability is no error at all, but just the natural result of random sampling.

Sample Size

The number of individuals in a sample. The sample size determines how well the sample represents the population, not the fraction of the population sampled.