1. Pre-Employment Drug Screening: Find the probability of selecting someone who got a result that it is a false negative. Who would suffer from a false negative result? Why?

2. Pre-Employment Drug Screening: Find the probability of selecting someone who got a result that it is a false positive. Who would suffer from a false positive result? Why?

3. Pre-Employment Drug Screening: Find the probability of selecting someone who uses drugs. Is this result close to the probability of 0.134 for a positive test result?

4. Pre-Employment Drug Screening: Find the probability of selecting someone who does not use drugs. Is this result close to the probability of 0.866 for a negative test result?

 

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217% AJIIILY LI ial LI It SuujtUL WIII yol a LUITTUL ICSUIL: Vvc VVII auuitSS sUuIT yucSIUI IS IT LI IS CIiapiLCI. False positive: Wrong result in which the • Test sensitivity: The probability of a true test incorrectly indicates the presence of a positive, given that the subject actually condition when the subject does not actu- has the condition being tested. ally have that condition. • Test specificity: The probability of a true • False negative: Wrong result in which the negative, given that the subject does not test incorrectly indicates that the subject have the condition being tested. does not have a condition when the sub- • Positive predictive value: Probability that ject actually does have that condition. a subject is a true positive, given that the 4-1 Review and Preview • True positive: Correct result in which the test yields a positive result (indicating that 4-2 Basic Concepts of test correctly indicates that a condition is the condition is present). Probability present when it really is present. Negative predictive value: Probability 4-3 Addition Rule • True negative: Correct result in which the that the subject is a true negative, given test correctly indicates that a condition is that the test yields a negative result (indi- 4-4 Multiplication Rule: not present when it really is not present. cating that the condition is not present). Basics • Prevalence: Proportion of subjects having 4-5 Multiplication Rule: some condition. Complements and Conditional Probability Table 4-1 Pre-Employment Drug Screening Results Negative Test Result (Drug Use Is Not Indicated) Positive Test Result 4-6 Counting (Drug Use Is Indicated) 4-7 Probabilities Through Subject Uses Drugs 44 Simulations (True Positive) (False Negative) (on CD-ROM) Subject Is Not a Drug User 90 860 (False Positive) (True Negative) 4-8 Bayes' Theorem 217% AJIIILY LI ial LI It SuujtUL WIII yol a LUITTUL ICSUIL: Vvc VVII auuitSS sUuIT yucSIUI IS IT LI IS CIiapiLCI. False positive: Wrong result in which the • Test sensitivity: The probability of a true test incorrectly indicates the presence of a positive, given that the subject actually condition when the subject does not actu- has the condition being tested. ally have that condition. • Test specificity: The probability of a true • False negative: Wrong result in which the negative, given that the subject does not test incorrectly indicates that the subject have the condition being tested. does not have a condition when the sub- • Positive predictive value: Probability that ject actually does have that condition. a subject is a true positive, given that the 4-1 Review and Preview • True positive: Correct result in which the test yields a positive result (indicating that 4-2 Basic Concepts of test correctly indicates that a condition is the condition is present). Probability present when it really is present. Negative predictive value: Probability 4-3 Addition Rule • True negative: Correct result in which the that the subject is a true negative, given test correctly indicates that a condition is that the test yields a negative result (indi- 4-4 Multiplication Rule: not present when it really is not present. cating that the condition is not present). Basics • Prevalence: Proportion of subjects having 4-5 Multiplication Rule: some condition. Complements and Conditional Probability Table 4-1 Pre-Employment Drug Screening Results Negative Test Result (Drug Use Is Not Indicated) Positive Test Result 4-6 Counting (Drug Use Is Indicated) 4-7 Probabilities Through Subject Uses Drugs 44 Simulations (True Positive) (False Negative) (on CD-ROM) Subject Is Not a Drug User 90 860 (False Positive) (True Negative) 4-8 Bayes' Theorem

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Given data information is as follows Positive Test Result ... View the full answer