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M12 Major Discussion: Induction, Abduction and Analogy Example for Statistical Generalization: A pharmaceutical company conducts a clinical trial to test the effectiveness of a new drug for treating a specific medical condition, such as high blood pressure. The company selects diverse participants, including different age groups, genders, and ethnicities, to ensure a representative sample for the general population. After rigorous testing, they found that the drug significantly reduced blood pressure in most participants. Based on statistical analysis, the company can generalize the findings to claim that the drug effectively treats hypertension in the broader population. In this example, the pharmaceutical company uses statistical generalization to conclude how effective the new drug will be for the entire population based on the results observed in a representative sample. Example for Hasty Generalization: Imagine someone who visits a new country and has a bad experience with a few locals. If this person quickly concludes that everyone in the country is unfriendly and unwelcoming, they are making a hasty generalization. This type of reasoning can lead to unfair stereotypes and judgments about a group of people without considering the diversity and individual differences within the population. Hasty generalizations often result from insufficient evidence or a failure to consider alternative perspectives and experiences. It's essential to recognize the limitations of our experiences and avoid drawing broad conclusions based on limited interactions. Example of Abduction/Inference to the Best Explanation: I will use a situation where my computer is not turning on. I observed that the power button did not respond, the screen was black, and there was no sound from the fan or the hard drive. I hypothesized that the power supply was faulty and that this was the cause of the problem. I compared this hypothesis with other possible explanations, such as a broken motherboard, a corrupted operating system, or a loose cable. I concluded that the power supply hypothesis was the best explanation because it explained all the observed facts, was more straightforward than the other hypotheses, and did not conflict with various well-established beliefs. Two virtues:
Simplicity is important because it helps us to avoid unnecessary assumptions and complications in our explanations. A more straightforward explanation is more likely to be true than a complex one, all other things equal. Falsifiability is important because it allows us to test our explanations empirically and revise them if the evidence proves false. A falsifiable explanation is more scientific and rational than an unfalsifiable one because observation can confirm or disconfirm. Peter Singer's Experiment Peter Singer, in my opinion unsuccessfully, tries an analogy between helping a drowning child and the obligation to donate money to a good cause in another country, trying to establish that due to the moral obligation to help others, both scenarios are analogous. These two scenarios are different. The drowning child is a situation that is happening right now, and in that moment, I can do something about it. There is an immediate gain compared to the alternative (losing the already-used shoes, the bike, etc.). It is happening right at that moment. One could argue that famine in a third-world country is also happening right now, but so is unemployment in the U.S. Am I also then morally obliged to donate to someone unemployed? Based on that, why is Peter Singer, and I'm talking about himself, not donating to unemployed people? Why are we all not are? Based on my statement above, it is evident that I do not think it is a good analogy.
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