Statistics - Ch. 1 Definitions

Front 1

Data

Back 1

observations (such as measurements, genders, survey responses) that have been collected.

Front 2

Statistics

Back 2

a collection of methods for 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 elements (scores, people, measurements, and so on) to be studied.

Front 4

Census

Back 4

the collection of data from every member of the population.

Front 5

Sample

Back 5

a subcollection of members selected from a population.

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 Data

Back 8

numbers representing counts or measurements.

Front 9

Qualitative Data

Back 9

data that can be separated into different categories that are distinguished by some nonnumeric characteristic.

Front 10

Discrete Data

Back 10

the number of possible values is either finite or a countable number (1,2,3...).

Front 11

Continuous Data

Back 11

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

Front 12

Voluntary response sample

Back 12

respondents themselves decide whether to be included.

Front 13

Observational study

Back 13

observe and measure specific characteristics, but do not modify subjects.

Front 14

Experiment

Back 14

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

Front 15

Experimental unit

Back 15

a subject.

Front 16

Cross-sectional study

Back 16

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

Front 17

Retrospective (case-control) study

Back 17

data are collected from the past by going back in time (through examination of records, interviews, and so on).

Front 18

Prospective (longitudinal or cohort) study

Back 18

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

Front 19

Confounding

Back 19

occurs in an experiment when you are not able to distinguish among the effects of different factors.

Front 20

Blinding

Back 20

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

Front 21

Double-blinding

Back 21

technique in which the subject or the administrator whether the treatment is real or placebo.

Front 22

Block

Back 22

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

Front 23

Randomized block design

Back 23

for use when conducting an experiment of testing one or more treatments, and there are different groups of similar subjects, but the groups are different in ways that are likely to affect the responses to treatments.

1. Form blocks.
2. Randomly assign treatments to subjects within each block.

Front 24

Completely randomized experimental design

Back 24

subjects are assigned to different treatment groups through a process of random selection.

Front 25

Rigorously controlled design

Back 25

subjects are very carefully chosen so that those given each treatment are similar in ways that are important to the experiment.

Front 26

Replication

Back 26

repetition of an experiment on sufficiently large groups of subjects.

Front 27

Random sample

Back 27

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

Front 28

Simple random sample

Back 28

sample 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 29

Probability sample

Back 29

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

Front 30

Systematic sampling

Back 30

select some starting point and select every nth (such as every 50th) element in the population.

Front 31

Convenience sampling

Back 31

use results that are easiest to get.

Front 32

Stratified sampling

Back 32

subdivide the population into at least two different subgroups (or strata) so that the subjects within the same stratum share the same characteristics (such as gender or age bracket), then draw a sample from each stratum.

Front 33

Cluster sampling

Back 33

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

Front 34

Multistage sample design

Back 34

involves the selection of a sample in different stages that might use different methods of sampling.

Front 35

Sampling error

Back 35

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

Front 36

Nonsampling error

Back 36

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