Exam 2 Vocab Flashcards
Decision Error
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.
Type I Error
Rejecting the null hypothesis when in fact it is true; getting a statistically significant result when in fact the research hypothesis is not true.
Alpha
The probability of making a Type I error; same as significance level.
Type II Error
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.
Beta
The probability of making a Type II error.
Effect Size
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)
Effect Size Conventions
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.
Meta Analysis
A statistical method for combining effect sizes from different studies.
Statistical Power
The probability that the study will give a significant result if the research hypothesis is true.
Power Table
A table for a hypothesis-testing procedure showing the statistical power of a study for various effect sizes and sample sizes.
t Test
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.
t Test for a Single Sample
A hypothesis-testing procedure in which a sample mean is being compared to a known population mean and the population variance is unknown.
Biased Estimate
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).
Degrees of Freedom
The number of scores minus 1. Written as df = N - 1
t Distribution
A mathematically defined curve that is the comparison distribution used in a t test.
t Table
A table of cutoff scores on the t distribution for various degrees of freedom, significance levels, and one- and two-tailed tests.
t Score
On a t distribution, the number of standard deviations from the mean (like a Z score, but on a t distribution).
Repeated-Measures Design
A research strategy in which each person is tested more than once; the same as the within subjects design.
t Test for Dependent Means
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.
Difference Scores
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.
Assumption
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.
Robustness
The extent to which a particular hypothesis-testing procedure is reasonably accurate even when its assumptions are violated.
t Test for Independent Means
A hypothesis-testing procedure in which there are two separate groups of people tested and in which the population variance is not known.
Distribution of Differences Between Means
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.
