Elementary Statistics 5Th Edition Chapter 1

Front 1

Data

Back 1

Collections of observations, such as measurements, genders, or survey responses

Front 2

Statistics

Back 2

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 measurements of data that are being considered.

Front 4

Census

Back 4

Collection of data from every member of a population.

Front 5

Sample

Back 5

Sub-collection of members selected from a population.

Front 6

Sampling Method:
Voluntary response (or self-selected)

Back 6

samples often have bias (those with special interest are more likely to participate).

Front 7

Statistical significance

Back 7

is achieved in a study when we get a result that is very unlikely to occur by chance.

Front 8

Practical significance

Back 8

Common sense might suggest that the finding does not make enough of a difference to justify its use of its use or to be practical.

Front 9

Potential Pitfalls

Back 9

Misleading conclusions
Too small of a sample used
Loaded questions were used
The order of the questions
Nonresponse
Missing Data
Precise Numbers
Percentages

Front 10

Parameter

Back 10

a numerical measurement describing some characteristic of a population.

Population = Parameter

Front 11

Statistic

Back 11

a numerical measurement describing some characteristic of a sample

Sample = Statsistic

Front 12

Quantitative Data

Back 12

(or numerical data)
Consists of numbers representing counts or measurements.

Ex: Weights, Ages

Front 13

Categorical Data

Back 13

(or qualitative data)
Consists of names or labels (representing categories)

Ex: gender, Shirt numbers on professional athletes uniforms

Front 14

Quantitative Data

Discrete Data

Back 14

results when the number of possible values is either a finite number or a 'countable' number

Ex: 0, 1, 2, 3, ...
The number of eggs that a hen lays

Front 15

Quantitative Data

Continuous (numerical) Data

Back 15

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

Ex: The amount of milk that a cow produces; e.g. 2.343115 gallons per day.

Front 16

Nominal Level

Back 16

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).

Ex: Survey responses yes, no, undecided.

Front 17

Ordinal Level

Back 17

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

Ex: Course grades A, B, C etc.

Front 18

Interval Level

Back 18

involves data that can be arranged in order and the difference between any two data values is meaningful. However, there is no natural zero starting point (where none of the quantity is present).

Ex: Years 1000. 2000, 1776 etc.

Front 19

Ratio Level

Back 19

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 the level, differences and ratios are meaningful.

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

Front 20

Observational study

Back 20

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

Front 21

Experiment

Back 21

apply some treatment and then observe its effects on the subjects.

Front 22

Simple Random Sample

Back 22

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

Front 23

Random Sample

Back 23

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 24

Systematic Sampling

Back 24

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

Front 25

Convenience Sampling

Back 25

Using results that are easy to get.

Front 26

Stratified Sampling

Back 26

Subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup.

Front 27

Cluster Sampling

Back 27

Divide the population area into sections (or clusters). Then randomly select some of those clusters. Now choose all members from selected clusters.

Front 28

Multistage Sampling

Back 28

Collect data by using some combination of the basic sampling methods.

In a multistage sample design, pollsters select a sample in different stages, and each stage might use different methods of sampling.

Front 29

Cross-sectional study

Back 29

Data are observed, measured, and collected at one point in time.

Front 30

Retrospective (or case control) study

Back 30

Data are collected from the past by going back in time.

Ex: examine records, interviews

Front 31

Prospective (or longitudinal or cohort) study

Back 31

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

Front 32

Randomization

Back 32

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 33

Replication

Back 33

is the repetition of an experiment on more than one subject.

Front 34

Blinding

Back 34

is a technique in which the subject doesn't know whether he or she is receiving a treatment or a placebo.

Front 35

Double-Blind

Back 35

1) The subject doesn't know whether he or she is receiving the treatment or a placebo.

2) The experimenter does not know whether he or she is administering the treatment or placebo.

Front 36

Confounding

Back 36

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

Front 37

Randomized Block Design

Back 37

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

Front 38

Matched Pairs Design

Back 38

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

Front 39

Rigorously Controlled Design

Back 39

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

Front 40

Sampling error

Back 40

the difference between a sample and the true population result, such an error results from chance sample fluctuations.

Front 41

Non-sampling error

Back 41

sample data incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly.