1.1-1.5

Question 1

Define statistics.

Answer 1

Statistics is the science of data. More specifically, it is the science of the collection, classification,
analysis, and interpretation of information/data.

Question 2

Why is it important to ensure statistics are being applied properly? Provide an example.

Answer 2

Must ensure statistics are being applied properly, otherwise, there can be catastrophic
effects
o Example: Sally Clark was falsely convicted of the murder of her own child due to
the improper use of statistical findings

Question 3

What are the two processes involved in statistics?

Answer 3

o Describing data sets
o Drawing conclusions from that data set

Question 4

Define measurement in statistics.

Answer 4

Measurement is the process we use to assign numbers to variables of individual
population units

Question 5

What are some ways measurements can be made>

Answer 5

▪ Using instruments
▪ Asking questions
▪ Ratings using scales

Question 6

Why is it important to know whether the data you are dealing with is qualitative or quantitative?

Answer 6

It is essential that you understand whether the data you are interested in are
quantitative or qualitative, since the statistical method appropriate for describing,
reporting, and analyzing the data depends on the data type (quantitative or qualitative).

Question 7

What are the three ways of obtaining data?

Answer 7

o Published source
o Designed experiment
o Observational study

Question 8

What are the two classes of variables?

Answer 8

Quantitative and qualitative.

Question 9

What are the two subclasses of quantitative variables?

Answer 9

discrete and continuous

Question 10

What are the two subclasses of qualitative variables?

Answer 10

Ordinal and nominal

Question 11

What are the four elements of a descriptive statistics problem?

Answer 11

1. The population or sample of interest
2. One or more variables (characteristics of the population or sample units) that are to be
   investigated
3. Tables, graphs, or numerical summary tools
4. Identification of patterns in the data

Question 12

What are the five elements of an inferential statistics problem?

Answer 12

1. The population of interest
2. One or more variables (characteristics of the population units) that are to be investigated
3. The sample of population units
4. The inference about the population based on information contained in the sample
5. A measure of the reliability of the inference

Question 13

What is a response value?

Answer 13

What we are interested in finding

Question 14

What is a sample size?

Answer 14

Number of participants in the study

Question 15

What is a population?

Answer 15

Set of all experimental units we are interested in studying

Question 16

What is a sample?

Answer 16

Subset of population of which we have actual observations

Question 17

Why do we use samples?

Answer 17

Use samples because it is often impossible to gather data on all experimental
units

Question 18

Define descriptive statistics.

Answer 18

Utilizes numerical and graphical methods to look for patterns in a
data set, to summarize the information revealed in a data set, and to present that
information in a convenient form.

Question 19

WHat is inferential statistics?

Answer 19

Utilizes sample data to make estimates, decisions, predictions, or
other generalizations about a larger set of data.

Question 20

What is a variable?

Answer 20

A characteristic or property of an individual experimental (or observational)
unit in the population.

Question 21

What is a census?

Answer 21

A study that includes a measurement of every experimental unit.

Question 22

What is a statisitical inference?

Answer 22

An estimate, prediction, or some other generalization about a
population based on information contained in a sample

Question 23

What is a measure of reliability?

Answer 23

A statement (usually quantitative) about the degree of
uncertainty associated with a statistical inference.

Question 24

Define qualntitative data

Answer 24

Data that are measured on a naturally occurring numerical scale.

Question 25

Differentiate between interval and ratio data.

Answer 25

Can be Interval Data – The origin has no meaning
or
o Ratio Data – Origin is a meaningful number

Question 26

Define qualitative data.

Answer 26

Measurements that cannot be measured on a natural
numerical scale; they can only be classified into one of a group of categories

Question 27

Differentiate between ordinal and nominal data.

Answer 27

Can be Nominal – Unable to be ranked
or
o Ordinal – Can be ranked/ordered

Question 28

What is a published source?

Answer 28

The data set of interest has already been collected.

Question 29

What are designed experiments?

Answer 29

• Conducted by the researcher and the units in the study have
  strict controls.

Question 30

WHat is an observational study?

Answer 30

Researchers observe experimental units in their natural settings
for an observational study and records the variables of interest. In this case, there are no
controls placed on the units

Question 31

What is a representative sample?

Answer 31

• Exhibits characteristics typical of those possessed by the target
  population.

Question 32

What is a simple random sample?

Answer 32

Simple Random Sample - of n experimental units is a sample selected from the
population in such a way that every different sample of size n has an equal chance of
selection.

Question 33

What is selection bias?

Answer 33

Selection bias occurs when some experimental units in the population have less chance
of being included in the sample than others

Question 34

What is nonresponse bias? When is it likely to happen?

Answer 34

a type of selection bias that results when data on all experimental
units in a sample are not obtained. In surveys where people choose whether or not they will respond.

Question 35

What is measurement error? Give an exmaple.

Answer 35

• inaccuracies in the values of the data collected. In surveys, the
  error may be due to ambiguous or leading questions and the interviewer’s effect on the
  respondent.

Question 36

What is a parameter?

Answer 36

A numerical summary of the population

Question 37

What are some drawbacks of random sampling>

Answer 37

Not everyone wants to participate.
Those selected may not be qualified to participate.
The random sample might not have enough range.

Question 38

Explain stratified random sampling.

Answer 38

The population is divided into non-overlapping subpopulations (strata).
Characteristics of cases are more similar within strata than across strata.
* Then take a simple random sample in each stratum.
* Combine the random selection from each stratum to make overall sample.

Question 39

Explain cluster sampling. When is this method commonly used?

Answer 39

The population is divided into large number of (small) clusters.
* We randomly sample a few clusters.
* We then choose all cases within the sampled clusters.
* This technique is usually used when the population is dispersed, e.g. across a wide geographic region.

Question 40

What is systematic sampling?

Answer 40

Cases are selected in an ordered sampling scheme.
* First case is selected at random, subsequent elements
follow a predetermined pattern.
* Selecting kth case from the list of all cases.

Question 41

What is the most common type of nonprobability sampling? Explain what it is.

Answer 41

Most common nonprobability sampling is volunteer sampling.
* when surveying (e.g., sending questionnaire) to a selection of people is not at random and receive volunteer
response.

Question 42

Describe the difference between a statistic and a parameter.

Answer 42

A parameter is a number describing a whole population (e.g., population mean), while a statistic is a number describing a sample (e.g., sample mean).

Question 43

Explain the difference between an explanatory variable and a response variable.

Answer 43

An explanatory variable is the expected cause, and it explains the results. A response variable is the expected effect, and it responds to other variables.