Hypothesis Testing

Front 1

a statistical method that uses sample data to evaluate a hypothesis about a population

Back 1

Hypothesis Testing

Front 2

1. State a hypothesis about a population. 2. Use the hypothesis to predict characteristics that the sample should have (set the criteria). 3. Obtain a random sample from the population. 4. Compare the obtained sample data with the prediction that was made from the hypothesis

Back 2

Hypothesis Testing Procedure

Front 3

States that in the general population there is no change, no difference, or no relationship in the context of an experiment. Predicts that the independent variable has no effect on the dependent variable for the population.

Back 3

Null Hypothesis

Front 4

States that there is a change, a difference, or a relationship for the general population. In the context of an experiment, it predicts that the independent variable does have an effect on the dependent variable

Back 4

Alternative Hypothesis

Front 5

Level of significance for the hypothesis test. Specific probability value, most common - .05, .01, & .001

Back 5

Alpha Level

Front 6

Composed of the extreme sample values that are very unlikely (as defined by the alpha level) to be obtained if the null hypothesis is true. The boundaries are determined by the alpha level. If sample data falls within it, the null hypothesis is rejected.

Back 6

Critical Region

Front 7

Use the alpha-level probability and the unit normal table to determine the exact locations

Back 7

Boundaries for the critical region

Front 8

Occurs when a researcher rejects a null hypothesis that is naturally true. Means that the researcher concludes that a treatment does have an effect when, in fact, it has no effect.

Back 8

Type I error

Front 9

Occurs when a researcher fails to reject a null hypothesis that is really false. The hypothesis test has failed to detect a real treatment effect. Magnitude of the effect is not big enough to move the sample mean to the critical region.

Back 9

Type II Error

Front 10

Allows you to reject the null-hypothesis when the difference between the sample and the population is relatively small, provided that the difference is in the specified direction.

Back 10

One-Tailed Test

Front 11

Requires a relatively large difference independent of direction. Used when there is no strong directional expectation or when there are two competing predictions.

Back 11

Two-Tailed Test

Front 12

Measurement of the absolute magnitude of a treatment, independent of a the size of the samples being used.

Back 12

Effect Size

Front 13

Effect size can be standardized by measuring the mean difference in terms of the standard deviation. Simply describes the size of the treatment effect and is not influence by the number of scores in the sample.

Back 13

Cohen's d

Front 14

The probability that the test will identify a treatment effect if one really exists

Back 14

Power

Front 15

Sample size, alpha level, one-tailed vs. two-tailed

Back 15

factors that affect power

Front 16

increase ___, increase power

Back 16

Sample Size

Front 17

Reducing ___, reduces power

Back 17

Alpha Levels

Front 18

Changing from a regular ____ to a _____ increases power

Back 18

One-tailed vs Two-Tailed