129 Where You’re Going In Chapter 3, you will learn how to determine the probability of an event. For instance, you will study conditional probability in Section 3.2. An important conditional probability in COVID-19 testing is the probability P(negative test|infected) that a person tests negative for COVID-19 given that the person is infected.This is known as a false negative result. False negative results allow people to believe they are not contagious when they are. The probability of a false negative result is affected by both the sensitivity of the test and the percentage of people infected. One test for COVID-19 has a sensitivity of about 75%, which means that 75% of infected people test positive for infection. The remaining 25% of infected people have false negative results. For a test sensitivity of 75%, the table shows how the probability that a person is infected affects the probability of a false negative result.The last column shows how many false negative results you can expect for 100 tests. Where You’ve Been In Chapters 1 and 2, you learned how to collect and describe data. Once the data are collected and described, you can use the results to write summaries, draw conclusions, and make decisions. For instance, during the early stages of the coronavirus disease 2019 (COVID-19) pandemic, testing was the primary means of minimizing the spread of the disease by identifying people who were infected and having them isolate. Like most lab tests, COVID-19 tests have a chance of producing false results. By collecting and analyzing data, scientists determined the accuracy of various tests to help decide which tests to use in different situations. P(infected) P(negative test|infected) False negatives expected for 100 tests 0.2 0.05 5 0.4 0.10 10 0.6 0.15 15 0.8 0.20 20
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