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13 Cards in this Set
- Front
- Back
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Type 1 error (Decision Errors)
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Reject null, when null is true
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Alpha level (Decision Errors)
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probability of making a type 1 error, held at .05
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Type 2 error (Decision Errors)
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retaining null when null is false
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Beta level (Decision Errors)
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probability of making a type 2 error
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Power
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correctly rejecting the null hypothesis
1- Beta (probability of rejecting the null when the null is false) calculated through being given cohen's d and the sample size of either an independent or dependent samples t-test. |
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Effects of increasing and decreasing alpha
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increased: beta becomes smaller, and power increases
decreased: beta becomes larger, and power decreases "Decreasing alpha to .01 decreased the power to detect a mean difference of .2 standard deviations with a sample size of 30 for an Independent Samples t-test. The power is now approximately 2.13%" |
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cohen's d
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absolute distance between means in terms of standard deviations.
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effect size/true mean difference
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increases: power increases
because it decreases beta which makes it less likely to make a type 2 error |
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effect size (small, medium, large)
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cohens d = .2 means an 85% overlap in the null and alt hypothesis graph
cohens d=.5 means a .67 percent overlap cohens d=.8 means a 53 percent overlap The larger cohen's d (distance between means), the less overlap. |
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Changing sample size
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has an effect on the sampling distribution of the mean
(increasing sample) size: less variance in scores, means smaller standard error, which (increases power) |
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how to make a certain power possible
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use power to find the non centrality parameter and then plug in the numbers to the non centrality parameter equation
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standard desired power
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.8
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Over lap (cohen's d/null and alt. graphs)
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high overlap between the null and alternative hypothesis means the means are closer together and more similar than different.
high overlap = low significance of effect low overlap = high significance of effect because the graphs are farther apart, meaning cohen's d is telling us that the means are too far apart to be from the same or similar distributions. |
