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1 PSY -260 - 7-2 Discussion: Statistics in Everyday Life Students' Name Institution Instructor Course Date

2 PSY -260 - 7-2 Discussion: Statistics in Everyday Life Type I errors, sometimes known as false positives, are shown throughout the film. The speaker cites jury verdicts as an example to demonstrate that, although it is very unlikely that an accused person is innocent, the jury may make a mistake. The speaker utilizes the context of medical testing to emphasize that just because a test result is positive does not necessarily signify that the patient has the condition in issue (Donnelly, 2005). A false positive, also known as a Type I error, is the incorrect result of a trial or test. The examples in the video are related to the programming emphasis on chance and uncertainty. The speaker contends that the scenario's inherent ambiguity and unpredictability implies that an incorrect choice or conclusion is always conceivable, whether in a jury decision or a medical exam. Type II errors, in my view, are the most dangerous to make. False negatives, also known as type II errors, arise when an individual or group is incorrectly identified as being free of a certain ailment or trait when they are not. The consequences of a person or society not receiving enough care or help are severe. Type I errors, on the other hand, occur when an individual or group is incorrectly labeled as having a disease or trait when they do not (Rubin, 2021). The possibility of a person or organization receiving unnecessary assistance pales in contrast to the much more serious consequence of failing to obtain assistance when it is really needed. In my perspective, the conditions surrounding a statistical decision have a role in identifying which of the errors is more serious. A false negative (Type II error) in the medical sector, for example, may result in the patient's serious illness not being recognized in time for proper treatment, while a false positive (Type I error) may result in unnecessary or even harmful

3 therapy or diagnosis. The consequences of a false positive or negative might vary dramatically depending on the situation. In my opinion, the consequences of committing Type I mistakes vs Type II errors vary depending on the setting. In the context of cancer treatment research, a false positive (Type I mistake) might lead to patients receiving unneeded or dangerous treatments, while a false negative (Type II error) could result in a cure being missed. A jury's judgment may result in the wrongful conviction of an innocent defendant due to a Type I mistake, or the acquittal of a guilty one due to a Type II error (Givati, 2019). It's possible to be late for work because of a false positive (Type I mistake), but it's also possible to be late because of a false negative (Type II error). Yes, my views on the importance of statistical reasoning for the general public have not altered. Knowing the probabilities of anything, from the occurrence of a natural catastrophe to the benefits and downsides of a medical operation, requires a strong understanding of statistical reasoning. It is critical for public policy issues such as voting, taxation, and resource allocation. Furthermore, the capacity to think statistically is required for making sense of the increasingly complex digital world and grasping the implications of data-driven decisions. That is why I believe statistical thinking is essential for effective citizenship.

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4 References Donnelly, P. (2005). How juries are fooled by statistics. https://www.ted.com/talks/peter_donnelly_how_juries_are_fooled_by_statistics? language=en Rubin, M. (2021). When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing. Synthese , 199 (3-4), 10969-11000. Givati, Y. (2019). Preferences for criminal justice error types: Theory and evidence. The Journal of Legal Studies , 48 (2), 307-339.

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