Estimation and Inference

Question 1

What are two broad types of sampling?

Answer 1

Probability sampling and non-probability sampling

Question 2

Why do we use samples?

Answer 2

It would be extremely expensive and time-consuming to examine every single population member

Question 3

What is probability sampling?

Answer 3

Every member has an equal chance of being selected in the sample which creates samples that are representative for the population

Question 4

What is non-probability sampling?

Answer 4

Depends on other factors than chance, like judgement or convenience which gives it a risk of a non-representative sample

Question 5

In general, which broad sampling technique leads to the most reliable and accurate sample?

Answer 5

Probability sampling

Question 6

What is simple random sampling and when is it useful?

Answer 6

Drawing a sample in such a way that everybody has an equal chance of being selected. Useful for homogeneous groups.

Question 7

Researcher who chooses to sample trades money and time for sampling error!!!

Sampling introduces error!!!

Question 8

What is a sampling plan?

Answer 8

The set of rules used to select a sample

Question 9

What is systematic sampling and when to use it?

Answer 9

Select every k’th member until you have a sample of desired size. Should result approximately random.

Useful for populations that are difficult to determine.

Question 10

What is sampling error?

Answer 10

The difference between the observed value of a statistic and the quantity it is intended to estimate as a result of using subsets of the population.

Question 11

What is stratified random sampling?

Answer 11

Population is divided into subpopulations based on specific classifications. Then, simple random sampling is performed from each of the subpopulations.

Question 12

What is cluster sampling?

Answer 12

Cluster sampling is a probability sampling technique where researchers divide the population into multiple groups (clusters) for research. So, researchers then select random groups with a simple random or systematic random sampling technique for data collection and unit of analysis.

Question 13

What is convenience sampling?

Answer 13

Data is selected at the convenience of researcher. Quick and low cost but reduced reliability

Question 14

What is judgmental sampling?

Answer 14

Researcher picks elements from population based on his knowledge.

Question 15

What is the difference between a parameter and a sample statistic?

Answer 15

Parameter describes a population statistic and not a sample

Question 16

What is the Central Limit Theorem?

Answer 16

Given a population described by any probability distribution having mean µ and finite variance σ2, the sampling distribution of the sample mean 𝑋⎯⎯⎯
computed from random samples of size n from this population will be approximately normal with mean µ (the population mean) and variance σ2/n (the population variance divided by n) when the sample size n is large.

Question 17

What are the three most important properties about the sample distribution?

Answer 17

The distribution of the sample mean will be approximately normal.
The sample mean will be approximately equal to the population mean.
Sample variance will be approximately equal to population variance divided by n

Question 18

How to calculate standard error of a sample?

Answer 18

Standard Deviation / SQRT Sample Size