Stats Chapter 10 Vocab

Front 1

Acceptance sampling

Back 1

Statistical procedure used in quality control and involves testing a batch of data to determine if the proportion of units having a particular attribute exceeds a given percentage.

Front 2

Alternative hypothesis

Back 2

Denoted by H1 or Ha, is the hypothesis that sample observations are influenced by some non-random cause.

Front 3

Confidence Interval

Back 3

Is an interval estimate combined with a probability statement;
statisticians use a confidence interval to express the degree of uncertainty associated with a sample statistic.

Front 4

Confidence level

Back 4

Refers to the percentage of all possible samples that can be expected to include the true population parameter.

Front 5

Critical values

Back 5

A factor used to compute the margin of error.

Front 6

Hypotheses testing

Back 6

A test that defines a procedure that controls the probability of incorrectly deciding that a default position (null hypothesis) is incorrect based on how likely it would be for a set of observations to occur if the null hypothesis were true.

Front 7

Level of significance

Back 7

The likelihood that a statistical test will reject the data and hypothesis, despite the hypothesis actually being true.

Front 8

Margin of error

Back 8

Expresses the maximum expected difference between the true population parameter and a sample estimate of that parameter. To be meaningful, the margin of error should be qualified by a probability statement (often expressed in the form of a confidence level).

Front 9

Null hypotheses

Back 9

Denoted by H0, is usually the hypothesis that sample observations result purely from chance.

Front 10

One-sample z statistic

Back 10

Any statistical test for which the distribution of the test statistic under the null hypothesis can be approximated by a normal distribution.

Front 11

Power

Back 11

The power of a statistical test is the probability that the test will reject the null hypothesis when the alternative hypothesis is true (i.e. the probability of not committing a Type II error).

Front 12

P-value

Back 12

The probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.

Front 13

Sample size

Back 13

The number of individuals included in a statistical survey.

Front 14

Significance level

Back 14

The probability that an effect is not likely due to just chance alone.

Front 15

Statistical significance

Back 15

A result is considered significant not because it is important or meaningful, but because it has been predicted as unlikely to have occurred by chance alone.

Front 16

Type I error

Back 16

Is the incorrect rejection of a true null hypothesis; it is a false positive.

Front 17

Type II error

Back 17

Occurs when the null hypothesis is accepted, but the alternative is true; that is, the null hypothesis, is not rejected when it is false.

Front 18

Z statistic

Back 18

A test score converted or transformed into a common scale, such as standard units, to effect a more reasonable scale of measurement in order to make comparisons between different tests; also known as standard measure.