Sample types
Basics
Possible errors
Experiment/Study
Things in a Experiment/Study

100

Choosing individuals who are easiest to reach results from

Convenience sample

100

the entire group of individuals about which we want information

Population

100

Sets bounds on the size of the likely error

Margin of Error

100

When individuals are observed but the variable of interest is not influenced.

Observational Study

100

A condition applied to individuals in an experiment

Treatment

200

People who choose themselves by responding to a general appeal

Voluntary Response Sample

200

The part of the population from which we collect information: draw conclusions about the entire populations

Sample

200

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

Undercoverage

200

Imposes a treatment on individuals and their responses to treatments are recorded

Experiment

200

The individuals to which the treatments are applied

Experimental Units

300

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

300

a representative sample from a large and varied population

Sample survey

300

When an individual chosen for the sample can’t be contacted/refuses to participate

Nonresponse

300

Assigns experimental units to the treatments strictly by chance

Completely Randomized design

300

Can be an inactive treatment group, an active treatment group, or a group that receives no treatment

Control Group

400

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

400

Systematically favoring certain outcomes

Bias

400

When an individual gives the wrong response

Response bias

400

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

400

A group of individuals, that are known before the experiment, to be similar in some way

Block

500

Divide the population into smaller groups. Clusters should mirror the characteristics of the population. Then choose and SRS of the clusters.

Cluster sample

500

Drawing a conclusion about a population from what we know about the sample

Inference

500

When some subjects respond favorably to any treatment, even a placebo

Placebo effect

500

A common type of block design used when we want to compare 2 treatments

Matched-pair design

500

Both the subject and the person measuring the response are unaware of what treatment was assigned

Double Blind