Topic 4- Sampling Methods And CLT

Question 1

2 concepts in inferential stats

Answer 1

Probability sampling
Sampling distribution

Question 2

4 types of probability samples

Answer 2

Simple random samplling
Systematic random sampling
Stratified random sampling
Clustered sampling

Question 3

Simple random sampling

Answer 3

Same probability of being included e.g random number generator

Question 4

Systematic random sampling

Answer 4

Random starting point selected, then every kth member is selected.

E.g every 10th customer in a restaurant

Question 5

Stratified random sampling

Answer 5

Divide population into subgroups, called strata, and sample selected randomly from each strata.

E.g divide population in school into ethnicity, then select randomly

Question 6

Cluster sampling

Answer 6

Divide population into clusters, then randomly select cluster, then randomly select sample from the clusters chosen

Difference from stratified is that clusters are randomly selected, so not all clustered may be hsed

Question 7

Sampling distribution

Answer 7

Probability distribution of a statistic for all possible samples of size N.

E.g sampling distribution of sample mean costs of all posssible means of a given sample size selected from a population.

Question 8

Sampling distribution of the sample mean

Answer 8

A probability distribution of all possible sample means of a given sample size.

Question 9

Central limit theorem

Answer 9

If samples of a particular size are selected from any population, the sampling distribution of the sample mean is approximate a normal distribution. (Improves with larger samples)

Question 10

Why is the CLT useful

Answer 10

Once we know sampling distribution of mean follows a normal distribution, we can find z scores and find the probability the sample mean falls within a region.

Question 11

Standard deviation of sample mean

Answer 11

σ/√N

N is sample size