Statistics in Everyday Life In this module, you are exploring ways that we use statistical reasoning and decision-making in our everyday lives. Decisions we make in everyday life are all based on probability—but often in an intuitive way. For example, if a family member gets a positive screening for cancer or another disease, this is a probabilistic determination based on the measurement made. Similarly, when police arrest individuals or juries convict them, they do so based on probabilistic determinations (e.g., how likely is it that this evidence would exist if the person were innocent?). Even mundane decisions like when to leave for work to arrive on time are based on probabilistic determinations. You cannot know that leaving at a certain time will mean arriving on time because there are a variety of variables you may fail to consider. However, you can make reasonable inferences based on probability, given the variables about which you are aware (e.g., traffic patterns, risk of accidents, existing construction, etc.). Clearly, statistical decision-making inundates our lives whether we are aware of it or not. First, you will watch a TED Talk that discusses real-world contexts in which statistical decision-making is crucial and consequential: How Juries Are Fooled by Statistics. https://www.ted.com/talks/peter_donnelly_how_juries_are_fooled_by_statistics?language=en (Note: Pay special attention to the video from 10:47 to end.) As you answer the discussion questions, think about the statistical concepts you learned in this module regarding statistical decision-making, such as true and false positives and negatives. You will write about the ways that statistical decision-making is involved in many areas of our daily life and demonstrate your understanding of these key concepts related to inferential statistical decision-making. For your initial post, review the video and address the following: How do the examples give 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. Which programmatic theme(s) tie in with the examples? In general, do you think that making Type I or Type II errors is worse? Do you think the context in which the statistical decision is being made affects which of the errors is worse? For example, if you think about scientific research into curing cancer, or jury decisions about criminal convictions, or scheduling decisions to get to work on time, do you feel that the negative effects of Type I and Type II errors are similar or different across these contexts? Given your earlier discussion about the importance of statistical thinking for effective citizenship and what you have learned in the course in general and this module specifically, do you still hold the same view about the importance of statistical thinking for the general population? Why or why not?

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In his Ted Talk Peter Donnelly discusses the impact of false positive and negative results on both the choices made by juries and the results of medical tests In the context of what we have been study... View the full answer