Chapter 1 - Intro To Statistics - 1.1-1.4

Front 1

Descriptive statistics

Back 1

A branch of statistics.

As a science, gathers, sorts, summarizes, and displays the data.

"Just the facts"

Front 2

Inferential statistics

Back 2

A branch of statistics.

As a science, involves using descriptive statistics to estimate population parameters.

Hint 2

Accuracy of inferences is dependent on how well the sample data represents population parameters.

Front 3

Qualitative data

Back 3

Consist of labels or descriptions of traits.

(Labeling or identifying - qualities)

Nominal and ordinal

Example:

Major of your study

Place of birth

Hair color

Hint 3

Also known as categorical data.

Front 4

Quantitative data

Back 4

Consist of counts or measurements.

(Counting and measuring - quantities)

Interval or Ratio

(Continuous or discrete data)

Example:

Price of textbook

Age

Miles driven to work

Front 5

Continuous data

Back 5

Data that can take on any value in a given range of numbers and are usually measurements.

(Usually measurements)

Example:

Heights of doors

Time spent in a day playing video games

Front 6

Discrete data

Back 6

Quantitative data that can take on only particular values and cannot take in the values inbetween.

(Usually counts)

Example:

The number of pets you have would be discrete data because you can have either 2 pets or 3 pets, not 2.75 pets.

Front 7

Levels of Measurement

Back 7

Nominal level (qualitative)

Ordinal level (qualitative)

Interval level (quantitative)

Ratio level (quantitative)

Front 8

Nominal level of measurement

Back 8

Data that represent whether a variable possesses some characteristic.

No calculations can be performed on data at the nominal level.

Labeling/categorizing a subject.

Example:

Maeve belongs in the category of females.

Jersey 15 labels Liam.

Hint 8

Qualitative, consisting of labels or names.

No natural order!

Front 9

Ordinal level of measurement

Back 9

Data that represent categories that have some associated order.

Calculations do not make sense.

Example:

2011 rankings of SEC football teams would be at the ordinal level (1: LSU, 2: Alabama, etc.)

Hint 9

Qualitative data

Have a natural order.

Front 10

Interval level of measurement

Back 10

If the data can be ordered and the arithmetic difference is meaningful.

(0 is only a placeholder -One exception: 0 Kelvin is absolute zero and indicates absence of heat, placing it in ratio level of measurement. )

Add and subtract only!

Example:

Comparing average temps of various cities, the data can be ordered, and temps could be calculated and interpreted. Phoenix has an average temp of 100 degrees, and Stockholm's average temp is 50 degrees. We can say Phoenix is hotter, and it is hotter by 50 degrees.

Hint 10

Quantitative data

Front 11

Ratio level of measurement

Back 11

Similar to interval data, except that they have a meaningful zero point and the ratio of two data points is meaningful.

(0 is meaningful - absence of something)

Add subtract multiply divide

Example:

Compare prices of 2 cars. If one costs $10,000 and the other $20,000, then the second car costs $10,000 more than the first and is twice as expensive.

Hint 11

Quantitative data

Front 12

Conducting a Statistical Study

Back 12

1) Determine design of the study.

a) state the question to be studied

b) determine the population and variables of interest

c) determine the sampling method and make sure the sample is representative of the population.

2) Collect the data.

3) Organize and describe the data

4) Analyze the data using inferential statistics to answer the question.

5) Identify any possible errors.

Hint 12

5 steps

Front 13

Observational study

Back 13

Observes data that already exist.

Hint 13

Example: A market research company wants to determine the average total time spent by teenagers on Facebook.

Front 14

Experiment

Back 14

Generates data to help identify cause - and - effect relationships.

Hint 14

Example: A study of the effect of oatmeal lowering blood pressure.

Front 15

Simulation

Back 15

Uses mathematical or physical model to reproduce the conditions of a situation or process.

Hint 15

(Can be considered as a type of experiment)

Front 16

Survey

Back 16

An investigation of one or more characteristics of a population.

(Can be considered as a type of observational study)

Front 17

Types of Observational Studies

Back 17

Cross - sectional study

Longitudinal study

Meta - analysis

Case study

Front 18

Cross - sectional study

Back 18

Data are collected at a single point in time

Front 19

Longitudinal study

Back 19

Data are gathered by following a particular group over a period of time.

Front 20

Meta - analysis study

Back 20

A study that looks at one variable over several previous studies.

Front 21

Case study

Back 21

Looks at multiple variables that affect a single event.

Front 22

Response variable

Back 22

The variable in an experiment that responds to the treatment.

Hint 22

Urge to smoke

Front 23

Explanatory variable

Back 23

The variable in an experiment that causes the change in the response variable.

Hint 23

Chantix

Front 24

Principles of Experimental Design

Back 24

1) Randomize the control and treatment groups.

2) Control for outside effects on the response variable.

3) Replicate the experiment a significant number of times to see meaningful patterns.

Hint 24

3 Principles

Front 25

Control group

Back 25

Group of subjects to which no treatment is applied in an experiment

Front 26

Treatment group

Back 26

A group of subjects to which researchers apply a treatment in an experiment

Front 27

Confounding variables

Back 27

Factors other than the treatment that cause an effect on the subjects of an experiment

Front 28

Placebo effect

Back 28

A response to the power of suggestion, rather than the treatment itself, by participants of an experiment.

Front 29

Placebo

Back 29

A substance that appears identical to the actual treatment but contains no intrinsic beneficial elements.

Front 30

Single - blind experiment

Back 30

Subjects do not know if they are in the control group or treatment group, but the people interacting with the subjects know which group each subject has been placed.

Front 31

Double - blind experiment

Back 31

Neither the subjects nor the people interacting with the subjects know to which group each subject belongs.

Front 32

Representative sample

Back 32

The same relevant characteristics as the population and does not favor one group from the population over another.

Front 33

Sampling methods

Back 33

Random sample

Simple random sample

Stratified sample

Cluster sample

Systematic sample

Convenience sample

Front 34

Census

Back 34

Asks all members of a neighborhood.

Front 35

Random sample

Back 35

A random sample is one in which every member of the population has an equal chance of being chosen.

Hint 35

Example: Drawing names from a hat.

Front 36

Simple random sample

Back 36

Every sample from the population has an equal chance of being chosen.

Front 37

Stratified sample

Back 37

One in which members of the population are divided in two or more groups, called strata, that share similar characteristics like age, gender, or ethnicity. A random sample from each stratum is then drawn.

Hint 37

A few members of each group.

Example: A pollster surveys 50 people in each of a senator's voting precincts.

Front 38

Cluster sample

Back 38

One in which population is divided in two or more groups called clusters, that are each similar to the entire population. The researcher then randomly selects some of the clusters.

Hint 38

Each member of a few groups.

Example: An educator chooses 5 of the school districts in the Chicago area and asks each household in those districts how many school - age children are in the home.

Front 39

Systematic sample

Back 39

A sample in which every nth member of the population is selected.

Hint 39

Should not be used if the population has a pattern, such as every other person on a list is female.

Front 40

Convenience sample

Back 40

A sample created by selecting members that are conveniently available.

Example:

A female student walks down the halls in her dorm asking students how much money they would spend in a food court in the dorm lobby in an effort to persuade the administration to offer such an option.

Hint 40

(convenience sampling often leads to non representative samples)

Front 41

Institutional Review Board (IRB)

Back 41

A group of people who review the design of a study to make sure that it is appropriate and that no unnecessary harm will come to the subjects involved.

Front 42

Informed consent

Back 42

Completely disclosing the goals and procedures involved in a study and obtaining agreement to participate.

Front 43

Bias

Back 43

Favoring of a certain outcome in a study.

Front 44

Sampling bias

Back 44

Occurs when the sample chosen does not accurately represent the population being studied.

Front 45

Dropouts

Back 45

Participants who begin a study but fail to complete it.

Front 46

Processing errors

Back 46

Errors that occur simply from the data being processed, such as typos when data are being entered.

Front 47

Nonadherents

Back 47

Participants who remain in the study until the end but stray from the directions they were given.

Front 48

Researcher bias

Back 48

Occurs when researcher influences the results of a study.

Front 49

Response bias

Back 49

Occurs when a researcher's behavior causes a participant to alter his/her response or when a participant gives an inaccurate response.

Front 50

Participation bias

Back 50

Occurs when there is a problem with either the participation - or lack thereof - of those chosen for the study.

Front 51

Nonresponse bias

Back 51

Occurs when there is a lack of participation in a self - selected sample from certain segments of a population, when a person refuses to participate in a survey, or when a respondent omits questions when answering a survey.