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The discussion was focused on skeptical challenges to science, particularly induction and underdetermination, and what they imply for science’s reliability. In my original post, I argued that these challenges don’t make me doubt science. Instead of having doubt about science, they show me that scientific knowledge is probabilistic rather than certain. Hume’s point about induction is true, we can’t logically prove that the future will resemble the past. However, as Lindsay mentioned, repeated and well-controlled evidence can justify reliance. For instance, medicine and engineering works by tracking error rates and are improved via testing. Reygan adds to this by saying that even if evidence alone can’t uniquely pick a theory (underdetermination), scientists use additional criteria like predictive success, simplicity, and coherence, which I think makes theory choice more rational instead of arbitrary.
Our group also connected these ideas to real practice. For instance, Makayla made a point about trust induction in everyday life, such as the run rising. It made me think about how induction is earned not by proof but by success and replication. Maya’s comparison to economics really helped as well; even when predictions fail, the scientific response is to refine rather than to claim certainty. Michael’s claim that “true science requires experiments” is good, but I think observation and simulations can still make a field informative even if controlled experiments are hard.
Overall, the discussion helped me see scientific knowledge as fallible but reliable. Science aims to give the best supported explanation given the current evidence, which improves over time. I think that skepticism don’t actually defeat science, but they help refine how we can justify the trust in science, which doesn’t require absolute certainty.
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