Chapter 6: Inferential Statistics

A set of flashcards covering key concepts from Chapter 6 on Inferential Statistics, focusing on definitions, sampling methods, and the Central Limit Theorem.

What is inferential statistics?

Using information from a sample to make inferences about a population.

Define population in research.

The total set of individuals, objects, groups, or events in which the researcher is interested.

What is a sample?

A subset of individuals selected from the population for research purposes.

What are probability sampling methods?

Sampling techniques where each member of the population has a known chance of being selected.

Give an example of simple random sampling.

Creating a list of CSUN students, numbering them, and choosing students using a table of random numbers or a computer program.

How is systematic sampling conducted?

Calculate a sampling interval, choose the first student at random, then select every kth individual.

What is stratified sampling?

A probability sampling method where the population is divided into subgroups, and samples are drawn from each stratum.

Define cluster sampling.

A probability sampling method that involves dividing the population into clusters and randomly selecting entire clusters for sampling.

What does the Central Limit Theorem state?

With a sufficiently large sample size, the sampling distribution of the mean is approximately normal, with a mean equal to the population mean.

What happens to the standard error of the mean with a larger sample size?

The standard error of the mean decreases in size.

What is the difference between parameter and sample statistics?

A parameter describes a characteristic of a population, while a sample statistic describes a characteristic of a sample.

What is sampling error?

Errors that occur when the researcher chooses a sample that is not representative of the population.

What is a non-sampling error?

Errors that arise in the data collection process due to factors other than drawing a sample.