How can we help?

You can also find more resources in our Help Center.

30 terms

A set of vocabulary words about sampling and collecting data.

Random

An event is this if we know what outcomes could happen, but not which particular values will happen.

Random Numbers

These are hard to generate. Nevertheless, several Internet sites offer an unlimited supply of these.

Simulation

This models random events by using random numbers to specify event outcomes with relative frequencies that correspond to the true real-world relative frequencies we are trying to model.

Simulation Component

The most basic situation in a simulation in which something happens at random.

Outcome

An individual result of a component of a simulation is this.

Trial

The sequence of several components representing events that we are pretending will take place.

Response Variable

Values of the this record the results of each trial with respect to what we were interested in.

Population

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

Sample

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

Sample Survey

A study that asks questions of a sample drawn from some population in the hope of learning something about the entire population.

Bias

Any systematic failure of a sampling method to represent its population is this.

Randomization

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

Matching

Any attempt to force a sample to resemble specified attributes of the population is a form of this. It may help make better samples, but it is no substitute for randomizing.

Sample Size

The number of individuals in a sample.

Census

A sample that consists of the entire population.

Population Parameter

A numerically valued attribute of a model for a population.

Statistic or Sample Statistic

There are values calculated for sampled data.

Representative

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

Simple Random Sampling (SRS)

This is when a sample size, n, is one where each set of n elements in the population has an equal chance of selection.

Sampling Frame

A list of individuals from whom the sample is drawn.

Sampling Variability

The natural tendency of randomly drawn sample to differ, one from another. Sometimes called Sampling Error, but it's not an error at all, just the natural results of random sampling.

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.

Cluster Sample

A sampling design in which entire heterogeneous groups are chosen at random.

Multistage Sample

Sampling schemes that combine several sampling methods.

Systematic Sample

A sample drawn by selecting individuals systematically from a sampling frame.

Voluntary Response Bias

Bias introduced to a sample when individuals can choose on their own whether to participate in the sample.

Convenience Sample

This consists of the individuals who are easily available.

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.

Nonresponse Bias

Bias introduced to a sample when a large fraction of those sampled fail to respond.

Resonse Bias

Anything in the survey design that influences responses falls under this heading.