A rare condition affects 1 out of every 100 people. The test for this condition has the following probabilities: If a person has the condition, the test is correct 95% of the time. If a person does not have the condition, the test gives a wrong result 10% of the time. If A is the event 'tested positive' and B is the event 'has condition,' find P(B'), P(AIB), and P(A|B').
Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 53m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample2h 19m
- 10. Hypothesis Testing for Two Samples3h 22m
- 11. Correlation1h 6m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
4. Probability
Bayes' Theorem
Problem 3.2.38
Textbook Question
"According to Bayes’ Theorem, the probability of event A , given that event B has occurred, is
P(A|B) = P(A) * P(B|A)P(A) * P(B|A) + P(A') * P(B|A').
In Exercises 33–38, use Bayes’ Theorem to find P(A|B).
38. P(A) = 12%, P(A') = 88%, P(B|A) = 66% , and P(B|A') = 19% "

1
Step 1: Recall Bayes' Theorem formula: P(A|B) = (P(A) * P(B|A)) / (P(A) * P(B|A) + P(A') * P(B|A')). This formula helps calculate the probability of event A occurring given that event B has occurred.
Step 2: Identify the given probabilities from the problem: P(A) = 0.12, P(A') = 0.88, P(B|A) = 0.66, and P(B|A') = 0.19.
Step 3: Substitute the given values into the numerator of the formula: P(A) * P(B|A) = 0.12 * 0.66.
Step 4: Substitute the given values into the denominator of the formula: P(A) * P(B|A) + P(A') * P(B|A') = (0.12 * 0.66) + (0.88 * 0.19).
Step 5: Divide the result of the numerator by the result of the denominator to find P(A|B). This will give you the probability of event A occurring given that event B has occurred.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Bayes' Theorem
Bayes' Theorem is a fundamental principle in probability theory that describes how to update the probability of a hypothesis based on new evidence. It states that the probability of event A given event B (P(A|B)) can be calculated using the formula P(A|B) = P(A) * P(B|A) / (P(A) * P(B|A) + P(A') * P(B|A')). This theorem is particularly useful in scenarios where prior knowledge about the events is available.
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Conditional Probability
Conditional probability is the measure of the probability of an event occurring given that another event has already occurred. It is denoted as P(A|B), which represents the probability of event A occurring under the condition that event B is true. Understanding conditional probability is crucial for applying Bayes' Theorem, as it allows us to assess how the occurrence of one event influences the likelihood of another.
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Prior and Posterior Probabilities
In the context of Bayes' Theorem, prior probability refers to the initial assessment of the likelihood of an event before new evidence is considered (P(A)), while posterior probability is the updated probability after taking into account the new evidence (P(A|B)). The distinction between these two types of probabilities is essential for understanding how new information can change our beliefs about the likelihood of events.
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