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Myers AP Statistics Chapter 4: "Designing Studies" - TPS5e
Sampling and Surveys
Terms in this set (43)
The design of a statistical study shows bias if it systematically favors certain outcomes.
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.
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.
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 (people don't answer), response bias (people lie), and errors due to question wording.
In a statistical study, the entire group of individuals about which we want information.
The use of chance to select a sample; is the central principle of statistical sampling; allows us to infer results to the population
A systemic pattern of incorrect responses; "people lie"
The part of the population from which we actually collect information. We use information from this 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. EX. bad sampling methods (convenience and voluntary response) and undercoverage
Simple random sample (SRS)
Gives every possible sample of a given size the same chance to be chosen. We often choose this type of sample by labeling the members of the population and using random digits to select the sample.
Groups of individuals in a population that are similar in some way that might affect their responses.
Stratified random sample
First classify the population into groups of similar individuals (mini populations). Then choose a separate SRS from each stratum to form the full sample.
Table of random digits
A long string of the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 with these properties:
• Each entry in the table is equally likely to be any of the 10 digits 0 through 9.
• The entries are independent of each other. That is, knowledge of one part of the table gives no information about any other part.
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.
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.
Completely randomized design
When the treatments are assigned to all the experimental units completely by chance.
When two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other.
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, this group may be given a placebo or an active treatment
Deliberately imposes some treatment on individuals to measure their responses.
A variable that helps explain or influences changes in a response variable.
The explanatory variables in an experiment are often known as these
A common form of blocking for comparing just two treatments. Often each subject receives both treatments in a random order. In others, each subject is matched to another subject who is very similar and results are compared.
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).
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. Allows us to infer cause and effect.
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.
A variable that measures an outcome of a study.
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.
Inference about cause and effect
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.
An OBSERVED effect so large that it would rarely occur by chance is called this.
In an experiment, between the subject(s) and those interacting & measuring their response to the treatments, if ONLY ONE party knows who is receiving which treatment & the other party doesn't.
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.
Principles of Experimental Design
The 4 basic principles for this are (in this order):
2. Random Assignment
First principle in Experimental Design that uses a design to look at 2 or more treatments
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