Population
the entire group of individuals about which we want information about
Sample
a subset of the population from which data are collected
Convenience sample
choosing individuals who are easiest to reach
Voluntary response sampling
people choose to be in the sample by responding to a general invitation
Bias
a design that is very likely to underestimate or overestimate the value you are measuring
Sampling without replacement
an individual from a population can be selected only once
Sampling with replacement
a selected individual is placed back into the population and could be chosen a second time
Simple random sample
a sample of size n selected from the population in such a way that each possible sample of size n has an equal chance of being selected.
Stratified random sample
selects a sample by choosing a SRS from strata, groups that share something in common, and combining to make a sample.
Cluster sampling
selects a sample by randomly choosing clusters, groups representative of the population, and including each member in the cluster
Systematic random sampling
randomly select an arbitrary starting point and then select every kth member of the population
Census
collects data from every individual in the population
Undercoverage
occurs when some members of the population are less likely to be chosen in a sample
Nonresponse
occurs when an individual chosen for the sample can't be contacted or refuses to participate
Response bias
occurs when there is a consistent pattern of inaccurate responses to a survey question
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
Response Variable
measures an outcome of a study
Explanatory variable
may help explain or predict changes in a 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
Factor
an explanatory variable that is manipulated and may cause a change in the response variable
Completely randomized design
the experimental units are assigned to the treatments completely by chance
Randomized block design
the random assignment of experimental units to treatments is carried out separately within each block
Matched pairs design
A method of assigning subjects to groups in which pairs of subjects are first matched on some characteristic and then individually assigned randomly to groups.
Comparison
an experiment needs to compare at least two groups
Random Assignment
use random change to assign experimental units to treatments
Control
keep other variables that might affect the response the same for all groups
Replication
each treatment group has sufficiently enough experimental units