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Lecture 11

Lecture 11.docx

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PHL 214
Luke Clark

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PHL182 Lecture: Week 11 (pg.228) Inductive claim: usually statistical Does not rule out other options possible (most, possibly, more likely) No unnecessary generalizations (hasty generalizations, i.e. slippery slope) Moving from general claims to particular, and from particular to general 75% of this class has been tested. The average IQ is 99, so it is very probable that this class has no geniuses in it. random sampling better 1000 to 1500 good representation of general public sample (75%) not representative of whole class Important: sample size and whether it is representative of the whole The more the sample size, the LESS room for error (graph goes down from top left to bottom right, with sample size on vertical axis and error % on horizontal axis) Most studies at 0.5% of error (good) Sampling: Sampleforming generalization (general conclusion) Pointing to shift in unit of analysis: comparing two different sample sizes (e.g. sample size of PHL class, and making conclusion with anatomy class) Challenging truth of conclusion: Examples: 1. Attaching the premises • Robin examined two layers of stra
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