PHL 131 Lecture Notes - Lecture 13: Conditional Probability, Within Reason, Fallacy
8 Jun 2020
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Conditional probability: conditional probability: the chances that an event will occur given that another event occurs, p(b|a) = p(a b) p(a, p(a|b) = p(a b) p(b) Simpson"s paradox: how averages and statistics can be extremely misleading: example: let"s say there are 2 football players and we are comparing their stats. One of them has 60 field goals and 70 touchdowns while the other has. 80 field goals and 50 touchdowns for the season. One might say that the first is a better player because they got more touchdowns (got more points from touchdowns). Regression fallacy: committed when one confuses a pattern in random events by overlooking regression effects (regression effects = naturally something will fluctuate downwards eventually): example: i fell down the stairs and twisted my ankle. My mom bought me a cooling/soothing gel and a few days later the pain went away.
