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9 Cards in this Set
- Front
- Back
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Define Type I error.
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Type I error, also known as an "error of the first kind", an α error, or a "false positive": the error of rejecting a null hypothesis when it is actually true. Plainly speaking, it occurs when we are observing a difference when in truth there is none. An example of this would be if a test shows that a woman is pregnant when in reality she is not. Type I error can be viewed as the error of excessive credulity.
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Define Type II error
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Type II error, also known as an "error of the second kind", a β error, or a "false negative": the error of failing to reject a null hypothesis when it is in fact not true. In other words, this is the error of failing to observe a difference when in truth there is one. An example of this would be if a test shows that a woman is not pregnant when in reality she is. Type II error can be viewed as the error of excessive skepticism.
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Sensitivity
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= TP/
TP+FN |
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Specificity
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=TN/
TN+FP |
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Positive Predictive Value
PPV |
=TP/
TP+FP |
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Negative Predictive Value
NPV |
=TN/
TN+FN |
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False positive Rate
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False positive rate (α) = FP / (FP + TN) = 18 / (18 + 182) = 9% = 1 − specificity
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False Negative Rate
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False negative rate (β) = FN / (TP + FN) = 1 / (2 + 1) = 33% = 1 − sensitivity
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Power (given β)
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P=1-β
P=sensitivity |
