4 Sampling and Sampling Distributions Free Online FlashCards

What is the difference between a population and a sample?

A population is the full group of interest; a sample is the subset observed.

How does a parameter differ from a statistic?

A parameter describes a population; a statistic is calculated from sample data.

What defines simple random sampling?

Simple random sampling selects individuals so every possible sample of the chosen size has an equal chance of selection.

How does systematic sampling work, and what can bias it?

Choose a random starting point in an ordered list, then select every kth individual. Repeating patterns in the list can bias results.

How does stratified sampling ensure subgroup representation?

Divide the population into meaningful subgroups, or strata, then randomly sample from each subgroup.

What is the defining process of cluster sampling?

Divide the population into clusters, randomly select some clusters, then survey everyone or a sample of people within them.

Why can convenience sampling produce biased results?

Convenience sampling selects people who are easiest to reach. It can systematically miss parts of the population, and a larger sample alone cannot fix that bias.

What does a sampling distribution describe?

It is the probability distribution of a statistic over all possible samples of a fixed size drawn using the same method.

What does the standard error measure?

The standard error is the standard deviation of a statistic’s sampling distribution; it measures sampling variability.

What are the mean and standard error of $\bar X$ for independent random observations?

For independent random observations, the sample mean has mean $\mu$ and standard error $\frac{\sigma}{\sqrt{n}}$.

What happens to the sample-mean standard error when sample size is quadrupled?

Because standard error decreases at a rate proportional to $\frac{1}{\sqrt{n}}$, quadrupling the sample size halves the standard error.

What is the standard error of a sample proportion?

For independent observations with population proportion $p$, the sample proportion has mean $p$ and standard error $\sqrt{\frac{p(1-p)}{n}}$.