Suppose that a particular type of cancer affects 1% of the population. There is a test for this cancer but it's not perfect: although the test gives a positive result for 95% of people who have the cancer, it also gives a positive result for 4% of the people who are cancer-free. Someone has just received a positive test result – what is the probability they have cancer?

A First Course in Probability (10th Edition)
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Author:Sheldon Ross
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Chapter1: Combinatorial Analysis
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Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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**Title: Understanding Conditional Probability in Medical Testing**

**Description:**

Suppose that a particular type of cancer affects 1% of the population. There is a test for this cancer, but it’s not perfect: although the test gives a positive result for 95% of people who have the cancer, it also gives a positive result for 4% of the people who are cancer-free. Someone has just received a positive test result – what is the probability they have cancer?

**Explanation:**

In this scenario, we are exploring the concept of conditional probability, which is crucial for understanding medical test results. The test has two types of errors: 

1. **False Positive Rate**: 4% of cancer-free individuals receive a positive test result.
2. **True Positive Rate**: 95% of individuals with cancer receive a positive test result.

Given these statistics, we are tasked with calculating the probability that a person actually has cancer given they've tested positive.
Transcribed Image Text:**Title: Understanding Conditional Probability in Medical Testing** **Description:** Suppose that a particular type of cancer affects 1% of the population. There is a test for this cancer, but it’s not perfect: although the test gives a positive result for 95% of people who have the cancer, it also gives a positive result for 4% of the people who are cancer-free. Someone has just received a positive test result – what is the probability they have cancer? **Explanation:** In this scenario, we are exploring the concept of conditional probability, which is crucial for understanding medical test results. The test has two types of errors: 1. **False Positive Rate**: 4% of cancer-free individuals receive a positive test result. 2. **True Positive Rate**: 95% of individuals with cancer receive a positive test result. Given these statistics, we are tasked with calculating the probability that a person actually has cancer given they've tested positive.
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