Chapter 4 - Designing Studies

Population

entire group of individuals we want information about

Census

Collects data from every individual in the population

Sample

Subset of individuals in the population from which we collect data

Sample survey

Study that collects data from a sample to learn about the general population from which the sample was selected

Random sampling

Method of sampling that uses a chance process to determine which members of a population are chosen for the sample

Observational study

Observes individuals and measures variables of interest, but does not attempt to influence the response 

Retrospective

Observational studies that examine existing data for a sample of individuals

Prospective

Observational studies that track individuals into the future

Experiment

Type of study that deliberately imposes treatments on experimental units to measure their responses

Simple random sample 

Type of sampling method where every group of individuals in the population has an equal chance to be selected as the sample

Sampling without replacement

An individual from a population can only be selected once. SRS uses this

Sampling with replacement

An individual from a population can be selected more than once.

Strata

Groups of individuals in a population that share characteristics thought to be associated with the variables being measured in a study.

Stratified random sample

A sample selected by choosing an SRS from each stratum and combining the SRs into one overall sample 

Cluster sample

Sample selected by randomly choosing clusters and including each member of the selected clusters in the sample

Cluster

group of individuals in the population that are located near each other

Systematic random sample

Sample selected from an ordered arrangement of the population by randomly selecting one of the first k individuals and choosing every kth individual after

Multistage sample

Sampling method that combines 2+ sampling methods 

Convenience sample 

Consists of individuals from the population who are easy to reach

Bias

A study being either likely to under or over estimate the value you want to know

Voluntary response sample

Consists of people who choose to be in the sample by responding to a general invitation.

Sampling frame

List that includes every member of a population

Under coverage

Occurs when some members of the population are less likely to be chosen or cannot be choses in a sample

Nonresponse

Occurs when an individual chosen for the sample can’t be contacted/does not participate 

Response bias

Occurs when there is a consistent pattern of inaccurate responses to a survey question

Explanatory Variable

May help explain or predict changes in response variable 

Response Variable

Measures outcome of study

Confounding variable

Occurs when 2 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 individuals in an experiment. If an experiment has several explanatory variables, it is a combination of specific values of these variables

Experimental units 

Is the object to which a treatment is randomly assigned.

Placebo

Treatment that has no active ingredient but is otherwise like other treatments

Subjects

Experimental units that are human beings 

Factor

an explanatory variable that is manipulated and may cause a change in the response variable

Levels

Different values of a factor

Control group

Used to provided a baseline for comparing the effect of other treatments. Can be an inactive treatment, an active one, or no treatment

Placebo effect

Describes the fact that some subjects in an experiment will respond favorable to any treatment, even if inactive

Double blind

Both subjects or the people who interact with them and measure the response variable don’t know which treatment a subject is receiving 

Single blind

Either subjects or the people who interact with them and measure the response variable don’t know which treatment a subject is receiving 

Completely randomized design

The experimental units are assigned to the treatments completely at random

Lurking variables

A variable absent from the study or not considered that effects the relationship between two variables

Comparison

Use a design that compares 2+ treatments. One of four principles in designing an experiment 

Random assignment

Means that treatments are assigned to experimental units using a chance process. One of four principles in designing an experiment 

Replication 

Means giving each treatment to enough experimental units so that a difference in the effects of the treatments can be distinguished from chance variation due to the random. One of four principles in designing an experiment 

Control

Keeping other variables constant for all experimental units. One of four principles in designing an experiment 

Block

A group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments

Randomized block design

The random assignment of experimental units to treatments is carried out separately within each block

Matched pairs design

Comparing two treatments that uses blocks of size 2.

Statistically significant

When observed difference in responses between the groups in an experiment is so large that is unlikely to be explained by chance variation in the random assignment

Scope of inference 

Random selection of individuals allows us to justify inference about the population while random assignment of individuals to justify inferences about cause + effect 

Random selection + assignment

Inferences about population + cause/effect

No random selection + assignment

No inferences 

No random selection + random assignment

Inference about cause/effect (conclude that […] causes […]

Random selection + no random assignment

Inferences about population (Population causes/tends…)