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The Practice of Statistics (fourth edition)- chapter 4
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Gravity
Terms in this set (51)
Anonymity
When the names of individuals participating in a study are not known even
to the director of the study.
Bias
The design of a statistical study shows bias if it systematically favors certain
outcomes
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.
Census
A study that attempts to collect data from every individual in the population.
Cluster sample
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.
Confidentiality
A basic principle of data ethics that requires individual data to be kept
private.
Confounding
When two variables are associated in such a way that their effects on a
response variable cannot be distinguished from each other
Control
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
administered
Control group
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.
Convenience sample
A sample selected by taking the members of the population that
are easiest to reach; particularly prone to large bias
Double-blind
An experiment in which neither the subjects nor those who interact with
them and measure the response variable know which treatment a subject received.
Experiment
Deliberately imposes some treatment on individuals to measure their
responses.
Experimental units
The smallest collection of individuals to which treatments are
applied.
Explanatory variable
A variable that helps explain or influences changes in a response
variable.
Factor
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.
Informed consent
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.
Level
A specific value of an explanatory variable (factor) in an experiment
Lurking variable
A variable that is not among the explanatory or response variables in a study but that may influence the response variable.
Matched pair
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
Nonresponse
Occurs when a selected individual cannot be contacted or refuses to
cooperate; an example of a nonsampling error.
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.
Observational study
Observes individuals and measures variables of interest but does not attempt to influence the responses.
Placebo
An inactive (fake) treatment
Placebo effect
Describes the fact that some subjects respond favorably to any treatment, even an inactive one (placebo).
Population
In a statistical study, the population is the entire group of individuals about which we want information.
Random assignment
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.
Random sampling
The use of chance to select a sample; is the central principle of
statistical sampling.
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.
Replication
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.
Response bias
A systemic pattern of incorrect responses
Response variable
A variable that measures an outcome of a study.
Sample
The part of the population from which we actually collect information. We use
information from a sample to draw conclusions about the entire population.
Sampling error
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.
Sample survey
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
sample
Sampling frame
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.
Single-blind
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
received.
Statistically significant
An observed effect so large that it would rarely occur by
chance
Strata
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.
Subjects
Experimental units that are human beings.
Treatment
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
Undercoverage
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.
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