{"id":"W4393039792","doi":"10.1037/met0000507","title":"The Bayes factor, HDI-ROPE, and frequentist equivalence tests can all be reverse engineered—Almost exactly—From one another: Reply to Linde et al. (2021).","year":2024,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Frequentist inference; Bayes factor; Rope; Bayesian probability; Bayes' theorem; Statistical hypothesis testing; Statistics; Equivalence (formal languages); Null hypothesis; Econometrics; Frequentist probability; Bayes' rule; Type I and type II errors; Mathematics; Bayesian inference; Computer science; Algorithm; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01134139,0.0005377302,0.001033635,0.00009486435,0.00017569,0.0004289724,0.0008676646,0.0003610027,0.001523518],"category_scores_gemma":[0.1612356,0.0003489009,0.0002380728,0.0005987626,0.0004661259,0.00009803714,0.0004047609,0.001252883,0.00007448737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008853965,"about_ca_system_score_gemma":0.00004686882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001172507,"about_ca_topic_score_gemma":0.00002629063,"domain_scores_codex":[0.9902847,0.005292278,0.001395623,0.001583614,0.000642711,0.0008010072],"domain_scores_gemma":[0.8774553,0.1203899,0.0002013628,0.001353768,0.000133997,0.0004656579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00039774,0.0007212316,0.000232153,0.0001715834,0.0007166613,0.0003193849,0.0007775679,0.000002098836,0.03854199,0.08809594,0.09960217,0.7704214],"study_design_scores_gemma":[0.0005359441,0.0006602589,0.005955241,0.0003660738,0.0001995008,0.00002790042,0.00005348482,0.0001354342,0.001880274,0.7139387,0.2756291,0.0006181348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.010546,0.002089619,0.9087788,0.06853925,0.003626299,0.001442623,0.001001676,0.0005459627,0.003429711],"genre_scores_gemma":[0.004426681,0.001326241,0.9644442,0.02836215,0.0003590778,0.0001605655,0.000005809596,0.0001029286,0.0008123835],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7698033,"threshold_uncertainty_score":0.9998963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6313980920682659,"score_gpt":0.6237437826978459,"score_spread":0.007654309370419998,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}