{"id":"W1966967512","doi":"10.1037/1082-989x.13.2.110","title":"A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes.","year":2008,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Manitoba Health; University of Manitoba","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; Manitoba Health Research Council","keywords":"Nonparametric statistics; Type I and type II errors; Heteroscedasticity; Statistics; Statistical hypothesis testing; Normality; Confidence interval; Robust statistics; Robustness (evolution); Mathematics; Statistic; Statistical power; Standard error; Variance (accounting); Nominal level; Econometrics; Sample size determination; Degrees of freedom (physics and chemistry); Multiple comparisons problem; Outlier","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.08726192,0.002687519,0.004246311,0.0074191,0.001571456,0.005123435,0.005255323,0.004226246,0.02252438],"category_scores_gemma":[0.3537436,0.001610286,0.004529857,0.006656958,0.003980364,0.004117398,0.004726139,0.008073249,0.007328836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001944217,"about_ca_system_score_gemma":0.004789508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001846862,"about_ca_topic_score_gemma":0.001815494,"domain_scores_codex":[0.8766762,0.08412058,0.006350829,0.01205593,0.02005544,0.0007409487],"domain_scores_gemma":[0.7373508,0.2013201,0.01688362,0.0282005,0.01522461,0.001020326],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006503986,0.000449422,0.003546485,0.007172451,0.004106434,0.0004347335,0.001665938,0.01258755,0.004977483,0.2259651,0.06215798,0.676286],"study_design_scores_gemma":[0.001018952,0.002599147,0.01108829,0.004615677,0.002313118,0.002232803,0.001147107,0.04582865,0.01270483,0.6963742,0.219452,0.0006251917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009695077,0.001774402,0.9869538,0.0007531752,0.000426692,0.001096357,0.0011242,0.0009110717,0.005990777],"genre_scores_gemma":[0.03905028,0.001936883,0.9451697,0.00131534,0.0004178502,0.008404836,0.001067389,0.0004965251,0.002141192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9127381,"threshold_uncertainty_score":0.4614906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5411151333206106,"score_gpt":0.547425561088569,"score_spread":0.006310427767958426,"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."}}