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13 Cards in this Set
- Front
- Back
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Alpha level
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The threshold P-value that determines when we reject a null hypothesis. Using an alpha level of (alpha), if we observe a statistic whose P-value based on the null hypothesis is less than (alpha), we reject that null hypothesis
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Alternative hypothesis
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The hypothesis that proposes what we should conclude if we find the null hypothesis to be unlikely
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Critical value
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The value in the sampling distribution model of the statistic whose P-value is equal to the alpha level. Any statistic value further from the null hypothesis value than the critical value will have a smaller P-value than (curly q letter) and will lead to rejecting the null hypothesis. The critical value is often denoted with an asterisk, as z*, for example.
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Effect size
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The difference between the null hypothesis value and the true value of a model parameter.
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Null hypothesis
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The claim being assessed in a hypothesis test. Usually, the null hypothesis is a statement of "no change from the traditional value," "no effect," "no difference," or "no relationship." For a claim to be a testable null hypothesis, it must specify a value for some population parameter that can form the basis for assuming a sampling distribution for a test statistic.
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One-proportion z-test
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A test of the null hypothesis that the proportion of a single sample equals a specified value (H(null): p=p null) by comparing the statistic z=(p hat-p null)/SD(p hat) to a standard Normal model
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One-sided alternative
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An alternative hypothesis is one-sided when we are interested in deviations in only one direction away from the hypothesized parameter value
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P-value
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The probability of observing a value for a test statistic at least as far from the hypothesized value as the statistic value actually observed if the null hypothesis is true. A small P-value indicates that the observation obtained is improbable given the null hypothesis and thus provides evidence against the null hypothesis
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Power
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The probability that a hypothesis test will correctly reject a false null hypothesis. To find the power of a test, we must specify a particular alternative parameter value as the "true" value. For any specific value in the alternative, the power is 1-Beta.
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Significance level
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Another term for the alpha level, used most often in a phrase such as "at the 5% significance level."
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Two-sided alternative
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An alternative hypothesis is two-sided when we are interested in deviations in either direction away from the hypothesized parameter value.
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Type I error
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The error of rejecting a null hypothesis when in fact it is true (also called a "false positive"). The probability of a Type 1 error is alpha.
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Type II error
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The error of failing to reject a null hypothesis when in fact is it false (also called a "false negative"). The probability of a Type II error is commonly denoted B(Beta) and depends on the effect size.
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