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21 Cards in this Set
- Front
- Back
Inferential statistics
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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.
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Null hypothesis
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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.
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Experimental hypothesis
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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.
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Rejecting the null hypothesis
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Concluding on the basis of statistical evidence that the null hypothesis is false.
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Failing to reject the null hypothesis
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Concluding on the basis if statistical evidence that the null hypothesis is true--that the independent variable does not have an effect.
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Type I error
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Erroneously rejecting the null hypothesis when it's true; concluding that an independent variable had an effect when, in fact, it did not.
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Alpha level
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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
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Statistically significant
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Finding that it is very unlikely to be due to error variance.
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Type II error
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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.
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Beta
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The probability of committing a Type II error (failing to reject the null hypothesis when it is false.)
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Power
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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.
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Power analysis
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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.
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Effect size
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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.
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t-test
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An inferential statistic that tests the difference between two means.
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Standard error of the difference between two means
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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.
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Critical value
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The minimum value if a statistic (such as t or F) at which the results would be considered statistically significant.
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Directional hypothesis
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A prediction that explicitly states the direction of a hypothesized effect; for example, a prediction of which two means will be larger.
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Nondirectional hypothesis
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A prediction that does not express the direction of a hypothesized effect--for example, which of two means will be larger.
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One-tailed test
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A statistic (such as t) used to test a directional hypothesis.
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Two-tailed test
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A statistical test for a nondirectional hypothesis.
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Paired t-test
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A t-test performed on a repeated measures design.
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