The Practice of Statistics- Chapter 4 – Flashcards
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When the names of individuals participating in a study are not known even to the director of the study.
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Anonymity
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The design of a statistical study shows bias if it systematically favors certain outcomes
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Bias
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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.
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Block
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A study that attempts to collect data from every individual in the population.
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Census
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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.
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Cluster sample
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When the treatments are assigned to all the experimental units completely by chance.
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Completely randomized design
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A basic principle of data ethics that requires individual data to be kept private.
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Confidentiality
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When two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other
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Confounding
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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
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Control
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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.
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Control group
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A sample selected by taking the members of the population that are easiest to reach; particularly prone to large bias
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Convenience sample
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An experiment in which neither the subjects nor those who interact with them and measure the response variable know which treatment a subject received.
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Double-blind
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Deliberately imposes some treatment on individuals to measure their responses.
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Experiment
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The smallest collection of individuals to which treatments are applied.
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Experimental units
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A variable that helps explain or influences changes in a response variable.
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Explanatory variable
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The explanatory variables in an experiment are often called factors.
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Factor
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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
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Inference about cause and effect
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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.
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Inference about the population
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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.
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Informed consent
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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.
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Institutional review board
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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.
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Lack of realism
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A specific value of an explanatory variable (factor) in an experiment
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Level
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A variable that is not among the explanatory or response variables in a study but that may influence the response variable.
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Lurking variable
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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.
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Matched pair
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A numerical estimate of how far the sample result is likely to be from the truth about the population due to sampling variability
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Margin of error
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Occurs when a selected individual cannot be contacted or refuses to cooperate; an example of a nonsampling error.
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Nonresponse
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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.
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Nonsampling error
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Observes individuals and measures variables of interest but does not attempt to influence the responses.
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Observational study
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An inactive (fake) treatment
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Placebo
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Describes the fact that some subjects respond favorably to any treatment, even an inactive one (placebo).
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Placebo effect
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In a statistical study, the population is the entire group of individuals about which we want information.
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Population
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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.
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Random assignment
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The use of chance to select a sample; is the central principle of statistical sampling.
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Random sampling
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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.
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Randomized block design
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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.
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Replication
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A systemic pattern of incorrect responses
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Response bias
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A variable that measures an outcome of a study.
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Response variable
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The part of the population from which we actually collect information. We use information from a sample to draw conclusions about the entire population.
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Sample
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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.
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Sampling error
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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
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Sample survey
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The list from which a sample is actually chosen.
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Sampling frame
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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.
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Simple random sample (SRS)
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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.
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Single-blind
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An observed effect so large that it would rarely occur by chance
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Statistically significant
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Groups of individuals in a population that are similar in some way that might affect their responses.
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Strata
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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.
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Stratified random sample
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Experimental units that are human beings.
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Subjects
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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
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Treatment
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Occurs when some members of the population are left out of the sampling frame; a type of sampling error
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Undercoverage
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People decide whether to join a sample based on an open invitation; particularly prone to large bias.
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Voluntary response samples
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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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Wording of questions
