The Practice of Statistics (fourth edition)- chapter 4
Terms in this set (51)
When the names of individuals participating in a study are not known even
to the director of the study.
The design of a statistical study shows bias if it systematically favors certain
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.
A study that attempts to collect data from every individual in the population.
To take a cluster sample, first divide the population into smaller groups.
Ideally, these clusters should mirror the characteristics of the population. Then choose an
SRS of the clusters. All individuals in the chosen clusters are included in the sample.
Completely randomized design
When the treatments are assigned to all the
experimental units completely by chance.
A basic principle of data ethics that requires individual data to be kept
When two variables are associated in such a way that their effects on a
response variable cannot be distinguished from each other
An important experimental design principle. Researchers should control for
lurking variables that might affect the response by using a comparative design and
ensuring that the only systematic difference between the groups is the treatment
An experimental group whose primary purpose is to provide a baseline
for comparing the effects of the other treatments. Depending on the purpose of the
experiment, a control group may be given a placebo or an active treatment.
A sample selected by taking the members of the population that
are easiest to reach; particularly prone to large bias
An experiment in which neither the subjects nor those who interact with
them and measure the response variable know which treatment a subject received.
Deliberately imposes some treatment on individuals to measure their
The smallest collection of individuals to which treatments are
A variable that helps explain or influences changes in a response
The explanatory variables in an experiment are often called factors.
Inference about cause and effect
Using the results of an experiment to conclude that
the treatments caused the difference in responses. Requires a well-designed experiment in
which the treatments are randomly assigned to the experimental units
Inference about the population
Using information from a sample to draw conclusions
about the larger population. Requires that the individuals taking part in a study be
randomly selected from the population of interest.
A basic principle of data ethics. Individuals must be informed in
advance about the nature of a study and any risk of harm it may bring. Participating
individuals must then consent in writing.
Institutional review board
A basic principle of data ethics. All planned studies must be
approved in advance and monitored by an institutional review board charged with
protecting the safety and well-being of the participants.
Lack of realism
When the treatments, the subjects, or the environment of an experiment
are not realistic. Lack of realism can limit researchers' ability to apply the conclusions of
an experiment to the settings of greatest interest.
A specific value of an explanatory variable (factor) in an experiment
A variable that is not among the explanatory or response variables in a study but that may influence the response variable.
A common form of blocking for comparing just two treatments. In some matched pairs designs, each subject receives both treatments in a random order. In others,
the subjects are matched in pairs as closely as possible, and each subject in a pair is
randomly assigned to receive one of the treatments.
Margin of error
A numerical estimate of how far the sample result is likely to be from
the truth about the population due to sampling variability
Occurs when a selected individual cannot be contacted or refuses to
cooperate; an example of a nonsampling error.
The most serious errors in most careful surveys are nonsampling
errors. These have nothing to do with choosing a sample—they are present even in a
census. Some common examples of nonsampling errors are nonresponse, response bias,
and errors due to question wording.
Observes individuals and measures variables of interest but does not attempt to influence the responses.
An inactive (fake) treatment
Describes the fact that some subjects respond favorably to any treatment, even an inactive one (placebo).
In a statistical study, the population is the entire group of individuals about which we want information.
An important experimental design principle. Use some chance
process to assign experimental units to treatments. This helps create roughly equivalent
groups of experimental units by balancing the effects of lurking variables that aren't
controlled on the treatment groups.
The use of chance to select a sample; is the central principle of
Randomized block design
Start by forming blocks consisting of individuals that are
similar in some way that is important to the response. Random assignment of treatments
is then carried out separately within each block.
An important experimental design principle. Use enough experimental units in each group so that any differences in the effects of the treatments can be distinguished
from chance differences between the groups.
A systemic pattern of incorrect responses
A variable that measures an outcome of a study.
The part of the population from which we actually collect information. We use
information from a sample to draw conclusions about the entire population.
Mistakes made in the process of taking a sample that could lead to
inaccurate information about the population. Bad sampling methods and undercoverage
are common types of sampling error.
A study that uses an organized plan to choose a sample that represents
some specific population. We base conclusions about the population on data from the
The list from which a sample is actually chosen.
Simple random sample (SRS)
The basic random sampling method. An SRS gives every
possible sample of a given size the same chance to be chosen. We often choose an SRS
by labeling the members of the population and using random digits to select the sample.
An experiment in which either the subjects or those who interact with them and measure the response variable, but not both, know which treatment a subject
An observed effect so large that it would rarely occur by
Groups of individuals in a population that are similar in some way that might affect their responses.
Stratified random sample
To select a stratified random sample, first classify the
population into groups of similar individuals, called strata. Then choose a separate SRS
from each stratum to form the full sample.
Experimental units that are human beings.
A specific condition applied to the individuals in an experiment. If an
experiment has several explanatory variables, a treatment is a combination of specific
values of these variables
Occurs when some members of the population are left out of the
sampling frame; a type of sampling error
Voluntary response samples
People decide whether to join a sample based on an open
invitation; particularly prone to large bias.
Wording of questions
The most important influence on the answers given to a survey.
Confusing or leading questions can introduce strong bias, and changes in wording can
greatly change a survey's outcome. Even the order in which questions are asked matters.