Chapter 2: Sampling Methods

These flashcards cover key concepts and definitions related to sampling methods as discussed in the lecture notes for Chapter 2.

What is a population in sampling methods?

The complete group of items for which information is required.

What is a sample?

A subset of the population from which data is collected.

What is a census?

A survey where the entire population is fully surveyed.

What is statistical inference?

Methods used to make conclusions about a population from sample data.

What type of sampling is known as 'simple random sample'?

A sampling method where each element has an equal chance of being selected.

What is stratified sampling?

A sampling method where the population is divided into strata and samples are taken from each.

What distinguishes probability sampling from non-probability sampling?

Probability sampling relies on random selection, while non-probability sampling does not.

What is sampling bias?

When certain parts of the population are not represented in the sample.

What is a convenience sample?

A sample that is taken which is convenient for the researcher and may not represent the population.

What is a judgement sample?

A sample taken based on the researcher’s judgement about who should be included.

What are the two main errors associated with sampling?

Sampling error and sampling bias.

What is a cluster sampling method?

A sampling method where the population is divided into clusters, and entire clusters are selected.

How can sampling observation errors occur?

Errors can occur during data collection due to inaccuracies in measuring instruments or respondent unreliability.

What should be considered when determining sample size?

The size must be sufficient to ensure reliability and representativeness of the sample.

What is the purpose of stratified sampling?

To ensure that different sub-groups of a population are adequately represented in the sample.