AP Statistics Chapter 4 vocabulary - designing studies

dotplot

a display of quantitative data in which each data value is shown as a dot above its location on a number line

bias

systematic error, favoring some parts of the population over others; systematically favors certain outcomes

sample mean

x̄, the average of a sample

sample

a subset of an entire population used to gather information about the population

statistic

a number describing a sample

population

entire group of individuals that we want information about

parameter

a number describing a population

sampling frame

list of individuals from which a sample is actually selected

census

a sample that collects information from every individual in the population

probability sample

a sample chosen by chance

simple random sample (SRS)

a sample which consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected; this gives every such sample and individual an equal chance to be chosen to avoid bias

stratified random sample

a sample constructed by dividing the population into groups (called strata) based on some characteristic, taking a SRS from each, and then combining these to form full sample [within each strata, individuals will be similar but when comparing one strata to another, the strata will vary from one another]

strata

groups formed of similar individuals; choosing a separate SRS from each of these groups and combining these SRSs creates a stratified random sample

cluster sample

take a SRS of several entire groups (clusters) of individuals that are "near" on another [within each cluster, individuals will be varied but when comparing one cluster to another, the clusters will be similar to one another]

systematic random sample

taking a sample by beginning at a randomly chosen location in a list and then every nth item thereafter

multistage sample

creating a sample by some combination of methods such as taking successive SRSs of smaller and smaller groups within the population in several steps

convenience sample

chooses individuals easiest to reach; one type of bad sample design; does not represent the entire population; biased because the person creating the sample is not selecting randomly

voluntary response sample

created by allowing people to choose themselves by responding to a general appeal; biased because people with extreme opinions are most likely to respond

explanatory variable or factor

a variable that may help explain or predict changes in a response variable (the variable that we change or manipulate in an experiment)

response variable or outcome

a variable that measures an outcome of a study

simulation

alternative method for producing data when an experiment or sample may be too dangerous, time consuming, or expensive

observational study

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

experiment

deliberately imposes some treatment on individuals in order to observe their responses

control

keep other variables that might affect the response variable the same in all groups

randomize

use chance to assign experimental units to treatments

replicate

use enough experimental units in each group so that any differences in the effects of treatments can be distinguished from chance variation in results

compare

use a design that compares two or more treatments

experimental units

the individuals on which the experiments are done

subjects

experimental units that are human beings

level

specific value of an explanatory variable or factor; combining levels of each factor creates the treatments

blinding

method used to ensure individual and/or experimenter doesn't/don't know who receives treatments

single blind

exactly one of these two groups, either the subjects or the people who have contact with them, know which treatment a subject received, but the other group does not know this information

double blind

neither the subjects nor the people who have contact with them know which treatment a subject received

confounded

to be mixed up with; condition when it is impossible to determine whether it is the explanatory or some other variable that is causing a change in the response variable

experimental design

the method used to assign treatments to groups in an experiment

undercoverage

some groups in population are left out of the process of choosing the sample

response bias

respondents are not truthful, whether by lying or by faulty memory

treatment

specific experimental condition (determined by a level or combination of levels of one or more factors) applied to the units

statistically significant

an observed effect so large that it would rarely occur by chance

completely randomized

when all experimental units are allocated at random among all treatments

matched pairs of design

a type of block design for comparing two treatments in which similar experimental units are paired (the treatments are randomly divided within each pair) or individuals are compared to themselves (each individual experiences both treatments, usually in random order)

randomized block design

design in which random assignment of treatments is carried out within similar groups of individuals called blocks

block

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

sampling

studying a part in order to gain information about the whole

nonresponse

individual chosen for the sample can't be reached or does not cooperate

sampling design

method used to choose the sample from the population

placebo

a treatment with no active ingredient used in experiments

causation

in principle, experiments can give good evidence for this

randomly choosing from the population lets us

make conclusions about the population

randomly assigning to treatment groups lets us

make conclusions about cause and effect