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11 Cards in this Set
- Front
- Back
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null hypothesis (Hsub0)
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predicts that the independent variable (treatment) has no effect on the dependent variable for the population
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alternative hypothesis (Hsub1)
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Hsub1 predicts that the independent variable (treatment) does have an effect on the dependent variable
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alpha level (level of significance)
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a probability value that is used to define the very unlikely sample outcomes if the null hypothesis is true
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critical region
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composed of extreme sample values that are very unlikely to be obtained if the null hypothesis if true; the boundaries for the critical region are determined by the alpha level; if sample data fall in the critical region, the null hypothesis is rejected
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Type I error
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occurs when a researcher reject a null hypothesis that is actually true; in a typical research situation, a Type I error means that the researcher concludes that a treatment does have an effect when in fact it has no effect
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alpha level (for a hypothesis test)
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the probability that the test will lead to a Type I error; the alpha level determines the probability of obtaining sample data in the critical region even though the null hypothesis is true
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Type II error
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occurs when a researcher fails to reject a null hypothesis that is really false; in a typical research situation, a Type II error means that the hypothesis test has failed to detect a real treatment effect
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(statistically) significant result
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very unlikely to occur when the null hypothesis is true; the result is sufficient enough to reject the null hypothesis; a treatment has a significant effect if the decision from the hypothesis test is to reject Hsub0
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one-tailed test (directional hypothesis test)
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the statistical hypothesis (Hsub0 or Hsub1) specify either an increase or a decrease in the population mean score; they make a statement about the direction of the effect
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effect size
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intended to provide a measurement of the absolute magnitude of a treatment effect, independent of the size of the sample(s) being used
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power (of a statistical test)
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the probability that the test will correctly reject a false null hypothesis; power is the probability that the test will identify a treatment effect if one really exists
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