Sample types
Choosing individuals who are easiest to reach results from
Convenience sample
the entire group of individuals about which we want information
Population
Sets bounds on the size of the likely error
Margin of Error
When individuals are observed but the variable of interest is not influenced.
Observational Study
A condition applied to individuals in an experiment
Treatment
People who choose themselves by responding to a general appeal
Voluntary Response Sample
The part of the population from which we collect information: draw conclusions about the entire populations
Sample
When some groups in the population are left out of the process of choosing the sample
Undercoverage
Imposes a treatment on individuals and their responses to treatments are recorded
Experiment
The individuals to which the treatments are applied
Experimental Units
Of size n consists of n individuals from population chosen in such a way that every set of n individuals has an equal chance to be in the sample selected
Simple Random Sample
a representative sample from a large and varied population
Sample survey
When an individual chosen for the sample can’t be contacted/refuses to participate
Nonresponse
Assigns experimental units to the treatments strictly by chance
Completely Randomized design
Can be an inactive treatment group, an active treatment group, or a group that receives no treatment
Control Group
Classify the population into groups of similar individuals (strata), then choose a separate SRS in each stratum and combine these SRS’s to form the full sample
Stratified Random Sample
Systematically favoring certain outcomes
Bias
When an individual gives the wrong response
Response bias
Blocking is used first to separate experimental units into groups based on some characteristics and then we proceed to randomly assign experimental units’ treatment within each block separately
Randomized Block Design
A group of individuals, that are known before the experiment, to be similar in some way
Block
Divide the population into smaller groups. Clusters should mirror the characteristics of the population. Then choose and SRS of the clusters.
Cluster sample
Drawing a conclusion about a population from what we know about the sample
Inference
When some subjects respond favorably to any treatment, even a placebo
Placebo effect
A common type of block design used when we want to compare 2 treatments
Matched-pair design
Both the subject and the person measuring the response are unaware of what treatment was assigned
Double Blind