Foundations of Statistical Inference I

These flashcards cover critical concepts from the lecture on statistical inference, focusing on inferential statistics, population parameters, sampling methods, and the relationship between sample size and error.

What are inferential statistics?

A set of procedures for deciding how closely the relationship observed in a sample corresponds to the unobserved relationship in the population.

What is a population in research?

The universe of cases that the researcher wants to describe.

What is a population parameter?

The characteristic of a population that a researcher is trying to figure out.

What is a sample?

A number of cases or observations drawn from a population to make inferences about the general population.

What is random sampling?

A method of selecting a sample in which every member of the population has an equal chance of being included.

What is selection bias?

A nonrandom process that creates compositional differences between the sample and the population.

What is random sampling error (standard error)?

The extent to which the sample statistic differs, by chance, from a population parameter.

How is random sampling error related to sample size?

Random sampling error is inversely related to sample size; as sample size increases, random sampling error decreases.

What does standard deviation represent?

The extent to which cases in a distribution fall close to the mean of that distribution.

How is standard error calculated?

Standard error of a sample mean is defined by the standard deviation divided by the square root of the sample size.