AP Statistics

Population

A statistical study of the entire group of individuals we want information about

Census

Collects data from every individual in the population

Sample

A set of individuals in the population from which we collect data

Sample Survey

A study that collects data from a sample to learn about the population from which sample was selected

Convenience sampling

Selects individuals from the population that are easy to reach

Bias

The design of a statistical study

Volunteer response sampling

Allows people to choose to be in the sample by responding to a general invitation

Random

Involves using a chance to process to determine are included in the sample

Sampling without replacement

An individual from a population can be selected only once

Sampling with replacement

An individual from a population can be selected more than once

Simple Random Sample (SRS)

Of size N is chosen in such a way that every group of N individuals in the population has an equal chance to be selected

Strata

Groups of individuals in a population who share characteristics thought to be associated with the variables being measured

Sampling

Selects a sample by choosing an SRS from each stratum and combining the SRS’s into one overall sample

Cluster

A group of individuals in the population that are located near eachother

Cluster Sampling

Selects a sample by randomly choosing clusters and including each member of the selected cluster

Systematic Random Sampling

Selects a sample from an ordered arrangement of the population by randomly selecting one of the first individuals and choosing every individual thereafter.

Undercoverage

Occurs when some members of the population are less likely to be chosen or cannot be chosen in a simple

Nonresponse

Occurs when an individual chosen for the sample can’t be connected or refuses to participate

Response Bias

Occurs when a systemic pattern of inaccurate answers to a survey question

Observational Study

Observes individuals and measures variables of interest but does not attempt to influence the responses

Response variable

Measures an outcome of a study

Explanatory variable

May help explain or predict changes in response variable

Experiment

An experiment deliberately imposes treatments (conditions) on individuals to measure their responses

Placebo

A treatment that has no active ingredient, but is otherwise like other treatment

Treatment

A specific condition applied to the individuals in any experiment

Experimental Units

The object to which a treatment is randomly assigned

Subjects

When the experiment units are human beings

Factor levels

In an experiment, a factor is an explanatory variable that is manipulated and may cause a change in the response variable. The different are called levels.

Control Group

Used to provide a baseline for comparing the effects of other treatments.

Placebo Effect

Describes the fact that some subject in an experiment will respond favorably to any treatment even an inactive

Double-blind

Neither the subjects nor those who interact with them and measure the response variable know which treatment a subject is receiving

Single-blind

Either subjects or the people who interact with them and measure the response variable don’t know which treatment a subject is receiving

Random Assignment

A random assignment means that the experimental units are assigned to treatments using a chance process

Control

Means keeping other variables constant for all experimental wins

Replication

Giving enough treatment to enough experiment units

Confounding

Occurs when two variables are associated in such way that their effects on a response variable cannot be distinguished from each other

Completely Randomized Design

The experimental units are assigned to the treatments completely at random

Block

A group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments.

Randomized block design

Random assignment of experimental units to treatments is carried out separately within each week

Matched pair design

A common experimental design for comparing two treatments that use block for size 2

Retroprespective

Examine existing data for a sample of individuals

Prospective

Observational studies that track individuals into the future

Sampling variability

The fact that different random samples of the same size from the same population produce different estimates

Statistically significant

Observed results of a study are too unusual to be explained by chance

Random selection

Individuals allow inference about the population from which the individuals were chosen

Random assignment

Individuals to groups allows inference about cause and effect