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philosophy of statistics philosophy of science replication crisis severity p-values error statistics severe testing statistical inference statistics significance tests likelihood principle r. a. fisher statistical significance tests deborah mayo foundations of statistics role of probability in inference bayes factors big data replicability neyman-pearson confidence intervals error probabilities asa 2016 statement on p-values statistical methodology biasing selection effects evidence reproducibility induction bayesian inference statistical reforms statistics wars replication paradox problem of induction association for psychological science (aps) 2015 c psychology higgs boson fisher j. neyman d. mayo reformulation of statistical tests popper richard royall experimental philosophy falsification frequentist statistics sir david cox lse ph500 asa task force statement 2021 default priors modeling machine learning p-hacking calibration casual inference e. pearson repligate stephen senn roles of probability in inference meta-methodology confirmation asa reproducibility psychology significance testing error statistics statistical inference data mining fallacies of rejection double-counting logic statistical inference as severe testing statistical fraud busting non-rejection error control capability & severity replication reforms capabilities of methods severity vs. rubbing off higgs discovery statistics war selection effects jeffreys-lindley paradox bayesian statistics background assumptions aris spanos frequentist inference likelihood stopping rules bayesian vs frequentist statistics testing reasoning fisherian tests data-dredging bernoulli trials reliability american statistical association duality of tests & confidence intervals (cis) revised role of error probabilities psa 2018 p-values exaggerate statistical crisis in science biased data novel evidence severity criterion predesignation use-constructed likelihood principle violations large-n problem esp-bem-bayes exercises parameter likelihood ratio formal epistemology carnap demarcation inductive logicians vs deductive testers enumerative induction asymmetry of falsification justifying induction uniformity of nature measures of confirmation logic of statistical inference paradox of irrelevant conjunctions glymour hacking frequentist principle of evidence (fev) bad evidence no test (bent) law of likelihood optional stopping unification valid vs. invalid argument premises modus tollens modus ponens disjunctive syllogism soundness logic of simple significance tests statistical modus tollens deductively valid argument categorical syllogism version of modus ponens detach conclusion affirming the consequent auxiliary hypotheses human medical observational studies failure to replicate phd stan young edwards deming data analysis scientific integrity geraerts "memory paper" affair smeesters affair debunking scientism statisticism mayo d. birnbaum logical flaws statistical foundations p values frequentists statistics probability scientific method(s) inference uses of probability estimation point interval estimation central limit theorem gold standard estimation hypothesis testing power severity excel program inductive inference null hypothesis fallacies of acceptance and rejection forensic anthropology histomorphology accept/reject procedures mis-specification (m-s) testing duhem's problem workshop on probability and learning probativism nancy reid normative epistemology fidicual peirce inductive logic n-p testing critical challenges-replies biased selection effects pre-registration overstate evidence against the null posterior probability sampling distribution intentions epidemiology industrialization of the scientific process bright lines paradigm shift data-driven science spurious correlation spanos replication technical activism nhst nonsignificant results liklihood principle connecting statistical claims to causal claims fraudbusting scientific methodology role for philosophers exploratory research reliablity false positivies bayes statistical fallacies error correction fallacies of non-statistically significant results columbia power vs severity post-data multiplicity in science gwas prior probabilities subgroup analyzes bonferroni economics 2 cultures misspecification testing weight of evidence ipcc margherita harris keynes mathematical abstractions invalid inference transparency math literally vs. researchers mixed models simulations right question complexity meta-analysis meta-science meta-research information-compression logic statistical tests' neyman pearson egon r.a. likelihood priniciple nature of probability multiplicity legitimate data-dredging psa 22 texas sharpshooter fallacy confidence distributions credible intervals frequentists inference min-ge xie suzanne thornton psa 2022 clark glymour data dredging regression graphical model searches multiple hypothesis tests cmu james o. berger statistical analysis statistical philosophy statistical testing in psych statistical testing research methods probativeness methodological probability bayesianism philosophy & practice neyman seminar jerzy neyman egon pearson problems of replication learning from error e. lehmann asa on p-values auditing model assumptions falsifiability ai ml artificial intelligence artificial intelliigence (ai) machine learning (ml) fanelli methods & theories use tools correctly don't ban tools best measures vary questionable research practice qrps changing methodology local inference local methodology grp-qrps-fraud abolish qrps evidential pluralism stephan guttinger evidential plualism fallacious inferences associations jon williamson gatekeepers errors malfunction disclaimers foundations theoretical statistics p-value controversy role for probability in statistical inference e. s. pearson 3 steps in n-p tests water plant example criticisms of p-values large sample size fallacies of non-rejection likelihoodists vs. significance testers frequentist feuds confidence intervals-problems new justification for ci fev/sev 5 sigman effect lindley o'hagan high energy particle physics severity interpretation of a rejection test t+ redefine statistical significance bayes/fisher disagreement relationship power & sample size j. berger and sellke do p-values exaggerate the evidence? casella and r. berger spike & smear sev large n-problem interpreting negative results sensitivity function howlers & chestnuts duality tests & conf. intervals 5 sigma effect statistical fluctuations fisher's testing principle p-value police sev principle for statistical significance look elsewhere effect (lee) key statistical conflicts fishing spurious p-values 21 word solution fisher-neyman & pearson dispute j. berger peace treaty diagnostic screening (ds) model of tests irreplication statistics battles ian hacking statistics debates erich lehmann gtr bibliometrics lemoine philosophy in science (pins) psa 2021 pradeu editors role bias replicability & significance wasserstein wsl 2019 editorial socially aware data science r.a. fisher fiducial probability skeptical user statistis wars yoav benjamini selective inference relevant varability nejm guidelines daniel lakens replication studies c. hennig mathematical modeling bent: bad evidence-no test p-value vs posterior jeffreys type prior berger and delampady contrasting bayes factors matching likelihood ratio generous to alternative default posterior probability david hand reproducibility crisis trust trustworthiness preregistration novelty bem meehl de groot registered reports covid-19 lab origins latham & wilson xu severe tests eclipse tests models