Module 1

Question 1

Statistics (singular)

Answer 1

way of reasoning, along with a collection
of tools and methods, designed to help us understand
the world with the use of data.

Question 2

statistics (plural)

Answer 2

calculations made from
data.

Question 3

data

Answer 3

values with a context.

Question 4

Why we need to collect data?

Answer 4

to find answers to the questions that cannot be
answered otherwise

or

an experiment usually results in different outcomes
(variability) and we want to study the reason(s) for the
variability

Question 5

population

Answer 5

entire collection of individuals

Question 6

sample

Answer 6

subset of the population, selected for study
in some prescribed manner

Question 7

parameter

Answer 7

numerical summary that describes a
characteristic of the population (often unknown)

Question 8

statistic

Answer 8

numerical summary that describes a
characteristic of the sample (is known once the data are
observed)

Question 9

Subjects or participants –

Answer 9

– people on whom we experiment

Question 10

Respondents

Answer 10

individuals who answer a survey

Question 11

Experimental units

Answer 11

animals, plants, and inanimate subjects

Question 12

Variables

Answer 12

characteristics recorded about each individual

Question 13

Types of Variables

Answer 13

Categorical
(Qualitative)

Numerical
(Quantitative)

Question 14

Categorical/Qualitative Variable

Answer 14

places a subject into one of several groups or
categories (or levels).

Question 15

Types of Categorical/Qualitative Variable

Answer 15

Nominal
Ordinal

Question 16

Nominal

Answer 16

the levels have no order

Question 17

Ordinal

Answer 17

the levels have some order

Question 18

Numerical/Quantitative Variables

Answer 18

measures a numerical quantity or amount in each subject

Question 19

types of Numerical/Quantitative Variables

Answer 19

Discrete
Continuous

Question 20

Discrete

Answer 20

can only take on distinct values

Question 21

Continuous

Answer 21

can take on any value in a given interval

Question 22

Census

Answer 22

special sample that includes everyone and “samples” the entire population.

Question 23

problems with taking a census:

Answer 23

too expensive

Undercoverage (may not actually include everyone) 

Too time-consuming

Question 24

sample statistics

Answer 24

summaries that are found from data in a sample

Question 25

types of conclusions you can make with sample statistics.

Answer 25

Population Inference Causal (cause-and-effect) Inference

Question 26

Population Inference

Answer 26

Results from the sample can be generalized to an entire population (as estimates)

Question 27

Causal (cause-and-effect) Inference

Answer 27

The difference in the responses is caused by the difference in treatments when comparing the results from two treatment groups.

Question 28

why is random sampling important?

Answer 28

eliminate the effect of unknown extraneous factors there's no way to fix a biased sample and no way to salvage useful information from it.

Question 29

Random Sampling Methods

Answer 29

Simple Random Samples (SRS) Stratified Random Sampling Systematic Random Sampling Cluster Random Sampling

Question 30

Simple Random Samples (SRS)

Answer 30

each sample of size n in the population has the same chance of being selected.

Question 31

sampling variability

Answer 31

Samples drawn at random generally differ from one another.

Question 32

Stratified Random Sampling

Answer 32

the population is first divided into strata; then take an SRS within each stratum before the results are combined.

Question 33

strata

Answer 33

different homogeneous groups

Question 34

benefits of Stratified Random Sampling

Answer 34

can reduce bias can also reduce the variability of our results.

Question 35

Systematic Random Sampling

Answer 35

Start from a randomly selected individual, then sample every Nth person.

Question 36

benefits of Systematic Random Sampling

Answer 36

Systematic sampling can be much less expensive than true random sampling.

Question 37

when is Systematic Random Sampling useful?

Answer 37

When there is no reason to believe that the order of the list could be associated in any way with the responses sought, systematic sampling can give a representative sample.

Question 38

Cluster Random Sampling

Answer 38

Splitting the population into clusters, select one or a few clusters at random and perform a census within each of them.

Question 39

cluster

Answer 39

Splitting the population into similar groups

Question 40

What type of sampling is this? a store owner decides to randomly test the hygiene of his staff. There are 10 departments in his store, and each department has 20 staff members. He plans to test 40 of his staff members by randomly choosing two departments and check everyone in these two departments

Answer 40

Cluster Random Sampling

Question 41

What type of sampling is this? you want to estimate the proportion of individuals that support federal party X in your area. You set up a booth in your area and ask every 50th person your question.

Answer 41

Systematic Random Sampling

Question 42

What type of sampling is this? you want to estimate the proportion of Canadians that support federal party X based on an appropriate representation from each province. You break up the population by province and select an SRS from each province.

Answer 42

Stratified Random Sampling

Question 43

What type of sampling is this? you put all the names of the individuals in the population in a box and draw names to complete the sample

Answer 43

Simple Random Samples (SRS)

Question 44

Bias

Answer 44

tendency for a sample to differ from the corresponding population in some systematic way.

Question 45

Sources of Bias:

Answer 45

Selection Bias Response Bias Voluntary Response Bias Nonresponse Bias

Question 46

Selection Bias

Answer 46

Undercoverage - when some portion of the population is not sampled at all or has a smaller representation in the sample than it has in the population.

Question 47

Response Bias

Answer 47

anything in the survey design that influences the responses. respondents may lie, especially if asked about illegal or unpopular behavior.

Question 48

Voluntary Response Bias

Answer 48

when individuals can choose on their own whether to participate in the sample.

Question 49

Nonresponse Bias:

Answer 49

when a large proportion of those sampled fail to respond.

Question 50

when can we make Causal (cause-and-effect) Inference?

Answer 50

when we have random allocation

Question 51

Lurking variables

Answer 51

variables that are related to both group membership and to the response. These are other variables that could possibly explain the result.

Question 52

two main types of study designs:

Answer 52

Observational Studies Randomized Experiment

Question 53

Observational Study

Answer 53

investigator observes individuals and measures variables of interest but does NOT attempt to influence the responses.

Question 54

Randomized Experiment

Answer 54

study design that allows us to prove a cause-and-effect relationship. `

Question 55

characteristics of an experiment:

Answer 55

Manipulates factor levels to create treatments. Randomly assigns subjects to these treatment levels. Compares the responses of the subject groups across treatment levels.

Question 56

Random Allocation

Answer 56

the individuals are randomly assigned to different treatment groups