population
the entire group of individuals about which we want information
sample
the part of the population from which we actually collect information. We use information from a sample to draw conclusions about the entire population.
convenience sample
choosing individuals who are easiest to reach
bias
occurs when the design of a statistical study systematically favors certain outcomes
voluntary response sample
consists of people who choose themselves by responding to a general appeal. Voluntary response samples show bias because people with strong opinions (often in the same direction) are most likely to respond.
simple random sample (SRS)
size n, 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
stratified random sample
a design in which the population is classified into groups of similar individuals, called strata. A separate SRS is used in each stratum, and these SRS's are combined to form the full sample.
cluster sample
the population is divided into smaller groups (clusters) which, ideally, should mirror the characteristics of the population. An SRS of the clusters is taken and all individuals in the chosen clusters are included in the sample.
undercoverage
occurs when some groups in the population are left out of the process of choosing the sample
nonresponse
occurs when an individual chosen for the sample can't be contacted or refuses to participate
response bias
leads to incorrect answers by respondents
inference
drawing conclusions about a population on the basis of sample data
wording of questions
has an important influence on answers
systematic random sample
systematic randomness! (survey every 10th person to walk by, for example).
multistage sample
multi-stratified. keep breaking up samples to get smaller representative samples; keep dividing.
census
asking everyone
observational study
observes individuals and measures variables of interest but does not attempt to influence the responses
experiment
deliberately imposes some treatment on individuals to measure their responses
lurking variable
a variable that is not among the explanatory or response variables in a study but that may influence the response variable
confounding
occurs when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other
treatment
a specific condition applied to the individuals in an experiment. (If an experiment has several explanatory variables, a treatment is a combination of specific values of these variables.)
experimental units
the smallest collection of individuals to which treatments are applied. When the units are human beings, they are often called subjects.
factors
explanatory variables
level
a specific value of each of the factors in an experiment (used to form different treatments, when experiments are studying the joint effects of several factors)
random assignment
experimental units are assigned to treatments at random, using some sort of chance process
completely randomized design
treatments are assigned to all the experimental units completely by chance
control group
provides a baseline for comparing the effects of the other treatments
replication
using enough experimental units to distinguish a difference in the effects of the treatments from chance variation
placebo effect
response to dummy treatment
double-blind experiment
neither the subjects nor those who interact with them and measure the response variable know which treatment a subject received
single-blind experiment
the subjects are unaware of which treatment they are receiving, but the people interacting with them and measuring the response variable do know. or vice versa
statistically significant
an observed effect so large that it would rarely occur by chance
randomized block design
experimental units are separated into blocks (a block is a group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response of the treatments), and random assignment of experimental units t
matched pairs design
creates blocks by matching pairs of similar experimental units, then uses chance to decide which member of a pair gets the first treatment. in some cases, each "pair" consists of just one experimental unit that gets both treatments one after the other, se
3 basic principles of experimental designs
control, randomization, replication
lack of realism
limits our ability to apply the conclusions of an experiment to the settings of greatest interest