Starnes, The Practice of Statistics, 6e, Chapter 4

census

Study that attempts to collect data from every individual in the population. (p. 221)

sample

Subset of individuals in the population from which we collect data. (p. 221)

population

In a statistical study, the entire group of individuals we want information about. (p. 221)

sample survey

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. (p. 222)

convenience sampling

Sample selected by taking from the population individuals that are easy to reach. (p. 223)

voluntary response sampling

A sample that consists of people who choose to be in the sample by responding to a general invitation. Voluntary response samples are sometimes called self-selected samples. (p. 224)

bias

The design of a statistical study shows bias if it is very likely to underestimate or very likely to overestimate the value you want to know. (p. 224)

random sampling

Using a chance process to determine which members of a population are chosen for the sample. (p. 225)

simple random sample

Sample chosen in such a way that every group of n individuals in the population has an equal chance to be selected as the sample. (p. 226)

stratified random sampling

Sample obtained by classifying the population into groups of similar individuals, called strata, then choosing a separate SRS in each stratum and combining these SRSs to form the sample. (p. 229)

cluster sampling

Sample obtained by classifying the population into groups of individuals that are located near each other, called clusters, and then choosing an SRS of the clusters. (p. 230)

undercoverage

Occurs when some members of the population are less likely to be chosen or cannot be chosen in a sample. (p. 233)

nonresponse

Occurs when an individual chosen for the sample can't be contacted or refuses to participate. (p. 233)

wording of questions

An important influence on the answers given in a survey. Confusing or leading questions can introduce strong bias, and changes in wording can greatly change a survey's outcome. (p. 234)

response bias

Occurs when there is a consistent pattern of inaccurate responses to a survey question. (p. 234)

observational study

Study that observes individuals and measures variables of interest but does not attempt to influence the responses. (p. 242)

confounding

When two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other. (p. 243)

experiments

A study in which researchers deliberately impose treatments on individuals to measure their responses. (p. 241)

placebo

A treatment that has no active ingredient but is otherwise like other treatments. (p. 244)

treatment

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. (p. 245)

experimental unit

The object to which a treatment is randomly assigned. When the experimental units are human beings, they are often called subjects. (p. 245)

subjects

Experimental units that are human beings. (p. 245)

factor

Explanatory variable in an experiment. (p. 246)

level

Specific value of an explanatory variable (factor) in an experiment. (p. 246)

comparison

Experimental design principle. Use a design that compares two or more treatments. (p. 247)

control group

Experimental group whose primary purpose is to provide a baseline for comparing the effects of the other treatments. (p. 248)

placebo effect

Describes the fact that some subjects respond favorably to any treatment, even an inactive one (placebo). (p. 249)

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. (p. 249)

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. (p. 249)

random assignment

Experimental design principle. Use chance to assign experimental units to treatments. (p. 251)

control

Experimental design principle that mandates keeping other variables that might affect the response the same for all experimental units. (p. 252)

replication

Experimental design principle. (p. 253)

completely randomized design

Design in which the experimental units are assigned to the treatments completely by chance. (p. 255)

block

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. (p. 257)

randomized block design

Experimental design begun by forming blocks consisting of individuals that are similar in some way that is important to the response. (p. 257)

matched pairs design

Common form of blocking for comparing just two treatments. (p. 260)

sampling variability

The fact that different random samples of the same size from the same population produce different estimates. (p. 270)

inference

Drawing conclusions that go beyond the data at hand. (p. 270)

margin of error

The difference between the point estimate and the true parameter value will be less than the margin of error in C% of all samples, where C is the confidence level. (p. 271)

statistically significant

When the observed results of a study are too unusual to be explained by chance alone, the results are called statistically significant. (p. 272)

anonymity

The names of individuals participating in a study are not known even to the director of the study. (p. 279)

confidential

A basic principle of data ethics that requires that an individual's data be kept private. (p. 279)

informed consent

Basic principle of data ethics that states that individuals must be informed in advance about the nature of a study and any risk of harm it may bring. (p. 279)

institutional review board

Board charged with protecting the safety and well-being of the participants in advance of a planned study and with monitoring the study itself. (p. 279)

inference about cause and effect

Conclusion from the results of an experiment that the treatments caused the difference in responses. (p. 280)

inference about a population

Conclusion about the larger population based on sample data. Requires that the individuals taking part in a study be randomly selected from the population of interest. (p. 280)