-
Notifications
You must be signed in to change notification settings - Fork 41
Lecture 7
sections 3.4 3.6 3.7 from "The Empire of Chance" ( https://data-ppf.slack.com/files/U3SJU2P6W/F9EF1UBM5/gigerenzer_eoc_ch3.pdf )
after this please do briefly read the primary sources:
Fisher, R. A. (1955), "Statistical Methods and Scientific Induction". Journal of The Royal Statistical Society (B) 17: 69-78. ( https://data-ppf.slack.com/files/U3SJU2P6W/F9EF20QJK/statistical_methods_and_scientific_induction_r.a._fisher-1955.pdf )
Neyman, J. (1956), "Note on an Article by Sir Ronald Fisher," Journal of the Royal Statistical Society. Series B (Methodological), 18: 288-294. ( https://data-ppf.slack.com/files/U3SJU2P6W/F9DH1TRBP/note_on_an_article_by_sir_ronald_fisher-1956.pdf )
Pearson, E. S. (1955), "Statistical Concepts in Their Relation to Reality," Journal of the Royal Statistical Society, B, 17: 204-207. ( https://data-ppf.slack.com/files/U3SJU2P6W/F9EF20763/statistical_concepts_in_their_relation_to_reality-1955.pdf )
-
Inference Experts: ONLY 3.4 3.6 3.7 are required
-
Fisher, R. A. (1955), "Statistical Methods and Scientific Induction". Journal of The Royal Statistical Society (B) 17: 69-78.
-
Neyman, J. (1956), "Note on an Article by Sir Ronald Fisher," Journal of the Royal Statistical Society. Series B (Methodological), 18: 288-294.
-
Pearson, E. S. (1955), "Statistical Concepts in Their Relation to Reality," Journal of the Royal Statistical Society, B, 17: 204-207.
- bitter rivalry with Neyman and Pearson
- a scientist, not just a mathematician
- claimed that Neyman and Pearson were mathematicians with insufficient real world experience
- did not view failure to reject null hypothesis (type II error) as an error
- basically doesn't view failure to reject null hypothesis as a final decision
- agreed with Neyman and Pearson on one point: Bayesians were wrong
- more mathematically rigorous: formalized hypothesis testing, type I & II errors, power of tests
- notable result is Neyman-Pearson lemma, which identifies the most powerful test at a given significance level
- rejected probabilistic interpretation of power of hypothesis tests. Hence the use of the term power