Question

watch a ted talk that discusses real-world contexts in which statistical decision making is crucial and consequential: how juries are fooled by statistics by peter donnelly in 2005

• how do the examples given in the video (jury decisions and medical tests) connect to what you learned about statistical decision making related to type i errors (false positives) and type ii errors (false negatives)? select either type i errors or type ii errors, and explain your response.

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Explanation:

To answer, we analyze the connection between the TED Talk examples (jury decisions, medical tests) and Type I/II errors. Let's pick Type I errors (false positives). In jury decisions, a Type I error occurs when an innocent defendant is wrongly convicted (rejecting a true "innocent" hypothesis). In medical tests, it's a healthy person being diagnosed as ill (false positive). These align with statistical decision - making: Type I error is rejecting a null hypothesis that's actually true, which matches the false positive scenarios in both contexts. For example, in the jury case, the null hypothesis could be "the defendant is innocent"; a Type I error is convicting (rejecting) an innocent (true null) defendant. In medical tests, the null could be "the patient is healthy"; a Type I error is diagnosing as ill (rejecting) a healthy (true null) patient.

Answer:

Type I errors (false positives) connect to the examples. In jury decisions, a Type I error is wrongly convicting an innocent defendant (rejecting the true "innocent" hypothesis). In medical tests, it's diagnosing a healthy person as ill (false positive, rejecting the true "healthy" hypothesis). Both match the statistical definition of Type I error (rejecting a true null hypothesis).