Unit 3

Question 1

What is the requirement for samples from a large population?

Answer 1

Samples must be random

This ensures that the sample accurately represents the population.

Question 2

What is ‘sampling without replacement’?

Answer 2

Each item/person can only be chosen once

This changes the probability of selection for subsequent items.

Question 3

What is ‘sampling with replacement’?

Answer 3

Every item has equal chance of being selected each time

This maintains the initial probability for each item throughout the sampling process.

Question 4

What is a simple random sample?

Answer 4

Every group of size (n) has equal chance of being chosen

Can be achieved by methods like drawing names from a hat or using random number tables.

Question 5

List two methods to choose a simple random sample.

Answer 5

• Name in hat
• Random number table or random number generator

Question 6

What is a potential drawback of a simple random sample?

Answer 6

It might not represent a diverse population

This can lead to biases in the results.

Question 7

What is a stratified random sample?

Answer 7

Divides population into groups (strata) and samples from each

Example: sampling from different categories of restaurants.

Question 8

What is the advantage of a stratified random sample?

Answer 8

Represents well if the population is diverse

However, it is not as random and takes more time.

Question 9

What is a cluster random sample?

Answer 9

Creates similar groups made of very mixed populations

Picks one group as a mini representation for the whole sample.

Question 10

What is the advantage of using a cluster random sample?

Answer 10

Faster and easier to implement

Represents pretty well, especially for non-diverse populations.

Question 12

What is a systematic random sample?

Answer 12

Every (n)th person/item

This method involves selecting individuals at regular intervals from a list.

Question 13

What is a census?

Answer 13

Selects all items/individuals

A census is comprehensive but time-consuming, typically only used with small populations.

Question 14

What is the major problem with sampling?

Answer 14

Bias

Bias can lead to certain responses being systematically favored over others.

Question 15

What is selection bias?

Answer 15

Sampling wasn’t random

This occurs when the sample does not accurately represent the population.

Question 16

What is survey bias?

Answer 16

Bias is in the survey

This type of bias affects the quality and reliability of survey results.

Question 17

What is voluntary response bias?

Answer 17

People volunteer with strong opinions

This leads to a skewed representation of the population’s views.

Question 18

What is convenience bias?

Answer 18

Bias from selecting individuals who are easiest to reach

This can result in a non-representative sample.

Question 19

What is undercoverage bias?

Answer 19

Certain groups are left out of the sample

This results in an incomplete representation of the population.

Question 20

What is non-response bias?

Answer 20

People are chosen but don’t respond

This can be mitigated by encouraging responses from selected individuals.

Question 21

What is response bias?

Answer 21

Problems leading to untruthful/incorrect data

Examples include broken tools, self-reported biases, and confusing questions.

Question 22

Who are the key participants in experimental design?

Answer 22

1. Researchers
2. Experimental units/subjects
3. Evaluators

Question 23

What is an explanatory variable?

Answer 23

Levels that are manipulated intentionally

Question 24

What are treatments in the context of experimental design?

Answer 24

Combinations of levels of the explanatory variable

Question 25

What is a response variable?

Answer 25

The outcome of the experiment

Question 26

What is a confounding variable?

Answer 26

A variable related to both the explanatory and response variables that can affect the results

Question 27

What are the four pillars of a good experimental design?

Answer 27

* Comparisons of two treatment groups * Random assignment of treatments to experimental units * Replication (more than one experimental unit per group) * Control of confounding variables and differences 2C2R

Question 28

What is a single-blind experiment?

Answer 28

Subjects do not know which treatment they are receiving

Question 29

What is a double-blind experiment?

Answer 29

Both subjects and evaluators do not know which treatment is being administered

Question 30

Why are control groups important in experiments?

Answer 30

They need placebos to assess the effect of the treatment

Question 31

Fill in the blank: People have a natural motivation to get better, so it is important not to tell them they are getting a _______.

Answer 31

placebo treatment

Question 33

What is Completely Randomized Design?

Answer 33

A design where each subject is assigned a number using random number generation.

Question 34

What is Randomized Block Design?

Answer 34

A design used when a lurking variable cannot be eliminated by random assignment, where subjects are grouped into blocks based on that variable.

Question 35

What is Matched Pair Design?

Answer 35

A design where subjects are paired based on similar variables, with one subject receiving treatment A and the other receiving treatment B randomly.

Question 36

What is the purpose of using random assignment in experimental design?

Answer 36

To create equivalent treatment groups and reduce bias.

Question 37

What does statistically significant mean?

Answer 37

It indicates that the results observed in the study are unlikely to have occurred by chance alone.

Question 38

How can one find statistically significant results?

Answer 38

By conducting statistical tests and analyzing p-values.

Question 39

Fill in the blank: In a Randomized Block Design, if a lurking variable cannot be removed by random assignment, it is accounted for by creating _______.

Answer 39

blocks of each.

Question 40

What is the role of statistical inference in experiments?

Answer 40

It involves taking statistics from a sample to infer conclusions about a larger population.

Question 41

What is the first step in conducting a perfect experiment?

Answer 41

Randomly selecting subjects.

Question 42

What is the second step in conducting a perfect experiment?

Answer 42

Randomly assigning subjects to treatment groups.