{"id":"W2143310476","doi":"","title":"Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions","year":2001,"lang":"en","type":"article","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Canada Council for the Arts; Université de Montréal; Natural Sciences and Engineering Research Council of Canada; Mitacs; Killam Trusts","keywords":"Nonparametric statistics; Monte Carlo method; Homogeneity (statistics); Statistical hypothesis testing; Sample size determination; Mathematics; Statistics; Statistical power; Probability distribution; Empirical distribution function; Applied mathematics; Econometrics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03622014,0.0009132226,0.003067052,0.00349958,0.001713374,0.003666569,0.003416912,0.002272576,0.0243882],"category_scores_gemma":[0.2026567,0.0006293887,0.003095022,0.003656873,0.005502034,0.006692471,0.003291283,0.003300135,0.002104664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001626654,"about_ca_system_score_gemma":0.002544918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537201,"about_ca_topic_score_gemma":0.001822751,"domain_scores_codex":[0.9460846,0.03578532,0.002192826,0.007002157,0.007183545,0.001751621],"domain_scores_gemma":[0.7272036,0.2332997,0.006631881,0.02783812,0.003899397,0.001127243],"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":[0.002956427,0.0007036707,0.06430972,0.001334453,0.002089431,0.001367279,0.002439383,0.05831217,0.007663418,0.316966,0.008067191,0.5337909],"study_design_scores_gemma":[0.000791701,0.003295085,0.09509713,0.0005210828,0.0006948801,0.001843589,0.002559372,0.3059889,0.0209519,0.539237,0.02856337,0.0004559961],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06063613,0.0005623105,0.9295736,0.0004798749,0.0001741491,0.0005503883,0.0008373329,0.0008772031,0.00630897],"genre_scores_gemma":[0.7101273,0.0003912645,0.2766353,0.0005593901,0.0003725086,0.002164076,0.001974952,0.0004306582,0.007344558],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03622014,"threshold_uncertainty_score":0.1915528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02096386410443803,"score_gpt":0.2763998397294424,"score_spread":0.2554359756250044,"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."}}