Exam 2 Vocab

Question 1

Decision Error

Answer 1

An incorrect conclusion in hypothesis testing in relation to the real (but unknown) situation, such as deciding the null hypothesis is false when it is really true.

Question 2

Type I Error

Answer 2

Rejecting the null hypothesis when in fact it is true; getting a statistically significant result when in fact the research hypothesis is not true.

Question 3

Alpha

Answer 3

The probability of making a Type I error; same as significance level.

Question 4

Type II Error

Answer 4

Failing to reject the null hypothesis when in fact it is false; failing to get a statistically significant result when in fact the research hypothesis is true.

Question 5

Beta

Answer 5

The probability of making a Type II error.

Question 6

Effect Size

Answer 6

A standardized measure of difference (lack of overlap) between populations. Effect size increases with greater differences between means. Written as d = (u1 - u2) [pop mean 1 minus pop mean 2] / sigma (the pop standard deviation)

Question 7

Effect Size Conventions

Answer 7

Standard rules about what to consider a small d = .2, medium d = .5, and large d = .8, effect size, based on what is typical in psychology research; also known as Cohen’s conventions.

Question 8

Meta Analysis

Answer 8

A statistical method for combining effect sizes from different studies.

Question 9

Statistical Power

Answer 9

The probability that the study will give a significant result if the research hypothesis is true.

Question 10

Power Table

Answer 10

A table for a hypothesis-testing procedure showing the statistical power of a study for various effect sizes and sample sizes.

Question 11

t Test

Answer 11

A hypothesis-testing procedure in which the population variance is unknown; it compares t scores from a sample to a comparison distribution called a t distribution.

Question 12

t Test for a Single Sample

Answer 12

A hypothesis-testing procedure in which a sample mean is being compared to a known population mean and the population variance is unknown.

Question 13

Biased Estimate

Answer 13

An estimate of a population parameter that is likely systematically to overestimate or underestimate the true value of a population parameter. For example, SD^2 would be a biased estimate of the population variance (it would systematically underestimate it).

Question 14

Degrees of Freedom

Answer 14

The number of scores minus 1. Written as df = N - 1

Question 15

t Distribution

Answer 15

A mathematically defined curve that is the comparison distribution used in a t test.

Question 16

t Table

Answer 16

A table of cutoff scores on the t distribution for various degrees of freedom, significance levels, and one- and two-tailed tests.

Question 17

t Score

Answer 17

On a t distribution, the number of standard deviations from the mean (like a Z score, but on a t distribution).

Question 18

Repeated-Measures Design

Answer 18

A research strategy in which each person is tested more than once; the same as the within subjects design.

Question 19

t Test for Dependent Means

Answer 19

A hypothesis-testing procedure in which there are two scores for each person and the population variance is not known; it determines the significance of a hypothesis that is being tested using difference or change scores from a single group of people.

Question 20

Difference Scores

Answer 20

The difference between a person’s score on one testing and the same person’s score on another testing; often an after-score minus a before-score, in which case it is also called a change score.

Question 21

Assumption

Answer 21

A condition, such as a population’s having a normal distribution, required for carrying out a particular hypothesis-testing procedure; a part of the mathematical foundation for the accuracy of the tables used in determining cutoff values.

Question 22

Robustness

Answer 22

The extent to which a particular hypothesis-testing procedure is reasonably accurate even when its assumptions are violated.

Question 23

t Test for Independent Means

Answer 23

A hypothesis-testing procedure in which there are two separate groups of people tested and in which the population variance is not known.

Question 24

Distribution of Differences Between Means

Answer 24

The distribution of differences between means of pairs of samples such that, for each pair of means, one is from one population and the other is from a second population; the comparison distribution in a t test for independent means.