Chapter 11

Statistic, sample statistic

Statistics are values calculated for sampled data. Those that correspond to, and thus estimate, a population parameter, are of particular interest. For example, the mean income of all employed people in a representative sample can provided a good estimate of the corresponding population parameter.

sampling frame

A list of individuals from whom the sample is drawn is called the sampling frame. Individuals who may be in the population of interest, but who are not in the sampling frame, cannot be included in any sample.

Population parameter

A measure of an attribute of a population.

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 populations is called a census

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 homogenous but are different from each other, a stratified sample may yield more consistently results that an SRS.

pilot study

A small trail 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 populations. Polls taken to assess voter preferences are commons sample surveys.

Sample

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

systematic sample

A systematic sample picks a random starting point and then chooses every nth member after that. The nth member [size of interval] is also chosen at random

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 spend.

Multistage sample

Combination of two or more sampling methods

Randomization

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

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 unfortunately, 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 fractions of the population sampled.

nonresponse bias

This type of bias occurs when an individual chosen to be in the sample cant be contacted or refuses to participate

Undercoverage bias

This type of bias occurs when some groups are left out of the sampling process when the sample is chosen

Voluntary response bias

This type of bias refers to people that often have strong opinions and who are more likely to respond in the same direction

Simple random sample

a SRS is a sample of n members chosen such that every member has an equal chance of being selected and every set of n members has an equal chance of being selected.

Convenience sampling

this amounts to choosing a sample because it is convenient or easy to do so

cluster sample

this is a sample randomly selected from clusters of a populations where each cluster is believed to be representative of the population

response bias

this type of bias refers to anything that influences the response

Voluntary response sample

this type of sample is formed by those individuals who choose to be in the sample