13.7: Glossary

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Bradley H. Dowden
California State University Sacramento

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appeal to a typical example: Drawing a conclusion about a population from the characteristics of a single example believed to be typical.
biased sample: A non-representative sample.

biased sampling method: A method of taking a sample that is likely to be non-representative
biased generalization: A generalization produced by relying on a biased sampling method.
coincidental pattern: A pattern in data that appears by accident. A coincidental pattern would not persist if more data were acquired.
confidence level: The percentage of confidence we need that the value of our statistic agrees with the target parameter, given the acceptable margin of error. For example, are we willing to be only 95 percent sure that we have the right answer, even allowing for the margin of error? Or must we be 99 percent sure?
disanalogies: The ways in which two things are not analogous
diversity: Variety.
extend the analogy: To point out additional ways in which two analogous things are alike.
fallacy of hasty generalization: Jumping to conclusions when the conclusion is a generalization from the evidence.
fallacy of jumping to conclusions: Drawing a conclusion prematurely or with insufficient evidence, even if the conclusion turns out to be true.
faulty analogy: Claiming that two things are analogous with respect to some characteristic when in fact they aren't analogous.
gambler’s fallacy: Assuming that an event is due or has a higher probability of occurring because it has occurred very much in the past, when it is should be known that the probability doesn’t change over time.
heterogeneous group: A group having considerable diversity in the relevant factors affecting the outcome of interest. For predicting the shape of a randomly picked snowflake, snowflakes are a heterogeneous group.
homogeneous group: A group with an insignificant amount of diversity in the relevant factors affecting the outcome of interest. For predicting either the color or the melting point of a randomly picked snowflake, snowflakes are a homogeneous group.
inductive generalization: Generalizing on a sample; also called induction by enumeration and empirical generalization.
margin of error: A limitation on the accuracy of a measurement; it is the interval around the parameter that the statistic falls within.
parameter: The target number in a measurement—that is, the true value of the characteristic being measured.
population: The set or group whose characteristics are the focus of the measurement or inductive generalization. The population need not be a group of people; when a quality control engineer samples cereal boxes to measure their freshness, the population is the cereal boxes.
principle of total information: When assessing the strength of an argument for a conclusion, use all the information that is relevant and available.
random sample: Any sample obtained by using a random sampling method.
random sampling method: Taking a sample from a target population in such a way that any member of the population has an equal chance of being chosen.
representative sample: Less formally, a sample having the same characteristics as the population. More formally, a sample S is a perfectly representative sample from a population P with respect to characteristic C if the percentage of S that are C is exactly equal to the percentage of P that are C. A sample S is less representative of P according to the degree to which the percentage of S that are C deviates from the percentage of P that are C.
sample: The subset of the population used to estimate the characteristics of the population.
simple statistical claim: A claim that has the form "x percent of the group G has characteristic C."
statistic: The number used as the estimate of the parameter.
statistically significant: A statistic that probably does not occur by chance.
stratified sample: A sample that is divided into strata or categories.
typical example: A single member that has the same characteristics as the population as a whole, in the sense that if it were the only member in a sample, the sample would be a representative sample of the population.
variable: Anything that comes in various types or amounts. There are different types of races, so race is a variable; there are different amounts of salaries, so salary is a variable; and so forth.
value of a variable: Each type or amount of a variable. For example, Caucasian is a possible value of the race variable; $30,000 would be the value of the salary variable for a person who makes $32,500 per year if the salary variable indicates annual salary only to the nearest $10,000.