Sampling
People who volunteer to be in a sample. This is biased.
Voluntary Response Sample
Individuals selected for a sample cannot be reached.
Nonresponse
The group of people who are given a placebo.
Control group
All experimental units are assigned to all of the treatments at random.
Completely randomized
The subject does not know whether they have the treatment or the placebo.
Choosing individuals who are easy to reach.
Convivence Sampling
Only those who choose to respond are studied
A dummy treatment used to see if something works. Looks the same as the real treatment.
Placebo
Individuals are broken into blocks then randomly assigned to treatments separately.
Block Design
Neither the subject nor the experimenter knows who has the treatment and who has the placebo.
Double-blind experiment
Divide population into group, perform a simple random sample in each group.
Stratified Random Sample
Choosing subjects that are easily accessible (this can lead to bias, is not a type of bias)
Convience Sample
An observed outcome is too large to occur by chance.
Statistically significant
Only two treatments done, each group gets both treatments
Matched pair design
What is being measured and used for comparison.
Response variable
Gives each person in a population a chance to be selected greater than zero.
Probability Sample
A group of the population is left out of the process when choosing a sample.
Undercoverage
Principles of experimental design
Control, randomization, replication
Applies a treatment to obtain a result. Observes the subject response when the treatment is administered.
Experiment
Specific values of a treatment
Level
Randomly choose the first person, then choose more people from the population at a regular interval.
Systematic Sample
The respondent or interviewer cause the questions to be dishonest.
Response bias
Lack of realism in experiments
Subjects, treatments, or setting is unrealistic in real life
Observes people and measures the variables of interest.
Observational study
What does this equation represent?
( Σ (x-μ)² ) / N
Population Varience