Chapter 1 (Intro to Statistics)
Descriptive: used to summarize and
describe data, do not involve generalizing beyond the data
at hand
Inferential: converts information about the sample into intelligent guesses about the
population,
make conclusions
Sample: small subset of a larger set of data, taken from the population
Population: larger set from which the sample is drawn, who you intend to learn about
Parameter: summary of information about the population
Statistic: summary information about a sample
Inferential statistics are based on the assumption that samples are random and represent
different segments of the population
Simple random sampling: every member of the population has an equal chance of selection
Random assignment: randomly divided into groups
Stratified sampling: used when population has a number of distinct "strata" or groups, used so
all subgroups in the sample are proportional to the sizes in the population
Cluster sampling: selecting a sample based on pre-organized groups
Convenience sampling: selecting a sample from whatever subjects are available
Variables: properties or characteristics of some event, object, or person can take on different
values or amounts
Independent variable: variable is manipulated by experimenter (aerobic training program)
Dependent variable: effect being measured, see what happens (effect on blood pressure)
Qualitative variable: those that express a qualitative attribute (hair color, religion, gender)
Quantitative variable: measured in terms of numbers (height, weight, shoe size)
Discrete variable: separate categories, can't be divided further; 4, 5, or 6 kids, not 4.53 kids