{"id":"W2919124814","doi":"10.22237/jmasm/1571659200","title":"Comparing Means under Heteroscedasticity and Nonnormality: Further Exploring Robust Means Modeling","year":2020,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; York University","funders":"","keywords":"Heteroscedasticity; Mathematics; Statistics; Type I and type II errors; Normality; Homogeneity (statistics); Monte Carlo method; Econometrics; Variance (accounting); Analysis of variance","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1732431,0.001621432,0.004001513,0.002214576,0.001112623,0.004321572,0.004282338,0.002479012,0.003587563],"category_scores_gemma":[0.4547995,0.0007713157,0.004174021,0.004027481,0.005084316,0.004190653,0.003276123,0.00467937,0.000473996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002032587,"about_ca_system_score_gemma":0.004995753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002387337,"about_ca_topic_score_gemma":0.001860511,"domain_scores_codex":[0.8360603,0.1360956,0.00479137,0.01018936,0.01159616,0.001267243],"domain_scores_gemma":[0.4697697,0.4753073,0.01788701,0.02686636,0.009388893,0.0007807076],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001925093,0.0007227371,0.03163582,0.003732544,0.007770463,0.001138413,0.01003087,0.09632422,0.003976952,0.376231,0.005046288,0.4614657],"study_design_scores_gemma":[0.0003982431,0.003000984,0.01850778,0.001140648,0.001360596,0.0005581013,0.001472771,0.3531282,0.005067369,0.6023961,0.01263841,0.0003307888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0145231,0.0008175418,0.9817238,0.0009405256,0.0001401611,0.0003950255,0.00009631614,0.0002139699,0.001149584],"genre_scores_gemma":[0.374166,0.001126491,0.6198035,0.0009166457,0.0002620158,0.002420807,0.0002414868,0.0002425365,0.0008206446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8267569,"threshold_uncertainty_score":0.9162081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8046739812709921,"score_gpt":0.5436600857329671,"score_spread":0.261013895538025,"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."}}