Statistics Chapter 1

statistics

is the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions

variable

a characteristic or attribute that can assume different values

data

the values a variable can assume

random variables

variables whose values are determined by chance

data set

collection of data values

data value

each value in a data set

population

all subjects that are being studied

sample

a group of subjects selected from a population

descriptive statistics

consists of the collection, organization, summarization, and presentation of data

inferenctial statistics

generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions

qualitative variable

variables that have distinct categories according to some characteristic or attitude

quantitative variable

variables that can be counted or measured

discrete variables

values that can be counted

continuous variables

can assume an infinite number of values between any two specific values. they are obtained by measuring. they can often include fractions and decimals

nominal level of measurement

classifies data into mutually exclusive (non-overlapping) categories in which no order or ranking can be imposed on the data

ordinal level of measurement

classifies data into categories that can be ranked; however, in precise differences between the ranks do not exist

interval level of measurement

ranks data, and precise differences between units of measure do not exist, but there is no meaningful zero. ratios of values are not meaningful

ratio level of measurement

possesses all the characteristics of interval measurement, and there is a true zero. in addition, true rations exist when the same variable is measured on two different members of the population

random sample

all members of the population have an equal chance of being selected

systematic sample

a sample obtained by selecting every kth member of the population where k is a counting number

stratified sample

a sample obtained by dividing the population into subgroups or strata according to some characteristic relevant to the study. the subjects are selected at random from each subgroup

cluster sample

obtained by dividing the population into sections or clusters and then selecting one or more clusters at random and using members in the cluster(s) as the members of the sample

sampling error

the difference between the results obtained from a sample and the results obtained from the population from which the sample was selected

nonsampling error

occurs when the data are obtained erroneously or the sample is biased

observational study

the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations

cross-sectional study

all the data are collected at one time

retrospective study

data are collected using records from the past

longitudinal study

data are collected over a period of time, say, past and present

experimental study

the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables

quasi-emperimental study

when random assignment is not possible, researchers use intact groups

independent variable (explanatory variable)

the one that is being manipulated by the researcher

dependent variable (outcome variable)

the resultant variable

treatment group

the group or groups for which the independent variable receives manipulation

control group

the group for which the independent variable is not manipulated

confounding variable

is one that influences the dependent or outcome variable but was not separated from the independent variable

blinding

subjects do not know whether they are receiving an actual treatment or the placebo

double blinding

both the subjects and researchers are not told which group is given the placebo

blocking

subjects are ordered into groups and then the treatment and placebo are randomly assigned within each group

completely randomized

subjects are assigned to groups randomly and the treatments are assigned randomly

matched-pair

one subject is assigned to a treatment group and another is assigned to a control group, but, before the assignment subjects are paired according to certain characteristics

replication

when the same experiment is done in another part of the country or different laboratory in order to further validate the study results

implied connections

use of words such as ‘may help’ suggest a connection between variables that does not actually exist

misleading groups

inappropriate graphs can misrepresent data and lead to false conclusions

faulty survey

how questions are phrased in a way that influences the way people answer them

changing the subject

using different values to represent the same data