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Tag Archives: modeling

Lars P. Syll — Time to abandon statistical significance

As shown over and over again when significance tests are applied, people have a tendency to read ‘not disconfirmed’ as ‘probably confirmed.’ Standard scientific methodology tells us that when there is only say a 10 % probability that pure sampling error could account for the observed difference between the data and the null hypothesis, it would be more ‘reasonable’ to conclude that we have a case of disconfirmation. Especially if we perform many independent tests of our hypothesis and they...

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Noah Smith — “Theory vs. Data” in statistics too

Important. I think Noah has this right. Fit the tool to the job, rather than the job to the tool. Aristotle defined speculative knowledge in terms of causal explanation. This definition stuck although Aristotle's analysis of causality did not. In the Posterior Analytics, Aristotle places the following crucial condition on proper knowledge: we think we have knowledge of a thing only when we have grasped its cause (APost. 71 b 9–11. Cf. APost. 94 a 20). That proper knowledge is knowledge...

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