Chapter 1 Definitions

Front 1

Data

Back 1

collections of observations (such as measurements, genders, survey responses)

Front 2

Statistics

Back 2

is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data

Front 3

Population

Back 3

the complete collection of all individuals (scores, people, measurements, and so on) to be studied

Front 4

Census

Back 4

Collection of data from every member of a population

Front 5

What do the values represent?
Where did the data come from?
Why were they collected?
An understanding of the context will directly affect the statistical procedure used.

Back 5

context

Front 6

parameter

Back 6

a numerical measurement describing some characteristic of a population.

Front 7

Statistic

Back 7

a numerical measurement describing some characteristic of a sample.

Front 8

Quantitative (or numerical) data

Back 8

consists of numbers representing counts or measurements.

Front 9

Example: The weights of supermodels
Example: The ages of respondents

Back 9

Quantitative (or numerical) data

Front 10

Categorical (or qualitative or attribute) data

Back 10

consists of names or labels (representing categories)

Front 11

Categorical

Back 11

Example: The genders (male/female) of professional athletes
Example: Shirt numbers on professional athletes uniforms - substitutes for names.

Front 12

result when the number of possible values is either a finite number or a ‘countable’ number

Back 12

Discrete data

Front 13

Continuous (numerical) data

Back 13

result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps

Front 14

Nominal level of measurement

Back 14

characterized by data that consist of names, labels, or categories only, and the data cannot be arranged in an ordering scheme (such as low to high)

Front 15

Ordinal level of measurement

Back 15

involves data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless

Front 16

Interval level of measurement

Back 16

With the additional property that the difference between any two data values is meaningful, however, there is no natural zero starting point (where none of the quantity is present)

Front 17

Ratio level of measurement

Back 17

the interval level with the additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present); for values at this level, differences and ratios are meaningful

Front 18

Example: Survey responses yes, no, undecided

Back 18

Nominal

Front 19

Example: Course grades A, B, C, D, or F

Back 19

Ordinal

Front 20

Example: Years 1000, 2000, 1776, and 1492

Back 20

Interval

Front 21

Example: Prices of college textbooks ($0 represents no cost, a $100 book costs twice as much as a $50 book)

Back 21

ratio

Front 22

categories only

Back 22

Nominal

Front 23

categories with some order

Back 23

Ordinal

Front 24

differences but no natural starting point

Back 24

Interval

Front 25

differences and a natural starting point

Back 25

ratio

Front 26

one in which the respondents themselves decide whether to be included

Back 26

Voluntary response

Front 27

observing and measuring specific characteristics without attempting to modify the subjects being studied

Back 27

Observational study

Front 28

Experiment

Back 28

apply some treatment and then observe its effects on the subjects; (subjects in experiments are called experimental units)

Front 29

Simple Random Sample

Back 29

of n subjects selected in such a way that every possible sample of the same size n has the same chance of being chosen

Front 30

Random Sample

Back 30

members from the population are selected in such a way that each individual member in the population has an equal chance of being selected

Front 31

Probability Sample

Back 31

selecting members from a population in such a way that each member of the population has a known (but not necessarily the same) chance of being selected

Front 32

selection so that each
individual member has an
equal chance of being selected

Back 32

Random Sampling

Front 33

Select some starting point and then
select every kth element in the population

Back 33

Systematic Sampling

Front 34

Convenience Sampling

Back 34

use results that are easy to get

Front 35

Stratified Sampling

Back 35

subdivide the population into at
least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum)

Front 36

Cluster Sampling

Back 36

divide the population area into sections
(or clusters); randomly select some of those clusters; choose all members from selected clusters

Front 37

Cross sectional

Back 37

data are observed, measured, and collected at one point in time

Front 38

Retrospective (or case control) study

Back 38

data are collected from the past by going back in time (examine records, interviews, …)

Front 39

Prospective (or longitudinal or cohort) study

Back 39

data are collected in the future from groups sharing common factors (called cohorts)

Front 40

Randomization

Back 40

is used when subjects are assigned to different groups through a process of random selection. The logic is to use chance as a way to create two groups that are similar.

Front 41

is the repetition of an experiment on more than one subject. Samples should be large enough so that the erratic behavior that is characteristic of very small samples will not disguise the true effects of different treatments. It is used effectively when there are enough subjects to recognize the differences from different treatments.

Back 41

Replication

Front 42

is a technique in which the subject doesn’t know whether he or she is receiving a treatment or a placebo. Blinding allows us to determine whether the treatment effect is significantly different from a placebo effect, which occurs when an untreated subject reports improvement in symptoms.

Back 42

Blinding

Front 43

The subject doesn’t know whether he or she is receiving the treatment or a placebo
The experimenter does not know whether he or she is administering the treatment or placebo

Back 43

Double Blind

Front 44

occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors.

Back 44

Confounding

Front 45

assign subjects to different treatment groups through a process of random selection

Back 45

Completely Randomized Experimental Design

Front 46

a block is a group of subjects that are similar, but blocks differ in ways that might affect the outcome of the experiment

Back 46

Randomized Block Design

Front 47

carefully assign subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment

Back 47

Rigorously Controlled Design

Front 48

compare exactly two treatment groups using subjects matched in pairs that are somehow related or have similar characteristics

Back 48

Matched Pairs Design