The Nature of Probability and Statistics

Statistics

Statistics

Science of conducting studies to collect, organize, summarize, analyze, and draw conclusions

Variable

Characteristics that can assume different values

Data

The values a variable can assume

Population

Consists of all subjects that are studied

Sample

Subset of population

Descriptive Statistics

Statistic that consists of collection, organization, summarization, and presentation of data (informative)

Inferential Statistic

Statistic that consists of generalizing from samples, perform estimations and hypothesis tests, determining relationships among variable and making predictions

Qualitative Data

Distinct categories of data according to an attribute (nonnumeric)

Quantitative Data

Data that can be counted or measured

Discreet

Type of quantitative data that can assume values that can be counted, but often has gaps

Continuous

Type of quantitative data that can assume an infinite number of values between any two specifications

Nominal

classifies data into non overlapping categories in which no order or ranking can be imposed

Ordinal

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

Interval

ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero

Ratio

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

Random Sampling

Each member of the population has an equal probability of being selected

Systematic Sampling

Selecting every kth member, where k is a counting number

Stratified Sampling

Dividing into subgroups by some characteristic relevant to study and then subjects are selected from each

Cluster Sampling

Dividing population into clusters then selecting one or more and using all members as sample

Observational Study

Study where the researcher observes what is or was happening and tries to draw conclusions

Experimental Study

Study where a researcher manipulates one of the variables and tries to determine how that affects other variables

Suspect Sample

Misuse of data where samples may not have been large enough or random

Ambiguous Averages

Misuse of data where common measurements are used when another should have been (mean, median, mode, range)

Changing Subject

Misuse of data where distortion can occur when different values are used to represent the same data

Detached Statistics

Misuse of Data where no real comparison is made

Implied Connections

Misuse of data where a claim is made to make someone think there is a connection between two variables where no connection actually exists

Misleading Graphs

Misuse of data where the information is shown inappropriately, so it can lead a reader to false conclusions

Independent/ Explanatory Variable

in an experimental study is the variable that is being manipulated by the researcher

Dependent / Outcome Variable

the resultant of the independent/ explanatory variable

Cofounding Variable

a variable that influences the dependent variable but was not separated from the independent variable