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21 Cards in this Set

  • Front
  • Back
Inferential statistics
Mathematical analyses that allow researchers to draw conclusions regarding the reliability and generalizability of their data; t-tests and F-tests are inferential statistics, for example.
Null hypothesis
The hypothesis that the independent variable will not have an effect; equivalently, the hypothesis that the means of the various experimental conditions will not differ.
Experimental hypothesis
The hypothesis that the independent variable will have an effect on the dependent variable; equivalently, the hypothesis that the means of the various experimental conditions will differ from one another.
Rejecting the null hypothesis
Concluding on the basis of statistical evidence that the null hypothesis is false.
Failing to reject the null hypothesis
Concluding on the basis if statistical evidence that the null hypothesis is true--that the independent variable does not have an effect.
Type I error
Erroneously rejecting the null hypothesis when it's true; concluding that an independent variable had an effect when, in fact, it did not.
Alpha level
The maximum probability that a researcher is willing to make a Type I error (rejecting the null hypothesis when it is true); typically, the alpha level is set at .05
Statistically significant
Finding that it is very unlikely to be due to error variance.
Type II error
Erroneously failing to reject the null hypothesis when it is false; concluding that the independent variable did mot have an effect when, in fact, it did.
Beta
The probability of committing a Type II error (failing to reject the null hypothesis when it is false.)
Power
The degree to which a research design is sensitive to the effects of the independent variable; powerful designs are able to detect effects of the independent variable more easily than less powerful designs.
Power analysis
A statistic that conveys the power or sensitivity of a study; power analysis is often used to determine the number of participants needed to achieve a particular level of power.
Effect size
The strength of the relationship between two or more variables, usually expressed as the proportion of variance in one variable that can be accounted for by another variable.
t-test
An inferential statistic that tests the difference between two means.
Standard error of the difference between two means
Statistical estimate of how much two condition means would be expected to differ if their difference is due only to error variance and the independent variable has no effect.
Critical value
The minimum value if a statistic (such as t or F) at which the results would be considered statistically significant.
Directional hypothesis
A prediction that explicitly states the direction of a hypothesized effect; for example, a prediction of which two means will be larger.
Nondirectional hypothesis
A prediction that does not express the direction of a hypothesized effect--for example, which of two means will be larger.
One-tailed test
A statistic (such as t) used to test a directional hypothesis.
Two-tailed test
A statistical test for a nondirectional hypothesis.
Paired t-test
A t-test performed on a repeated measures design.