{"id":"W2340675989","doi":"10.1002/cjs.11274","title":"Sample‐size calculation for tests of homogeneity","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Homogeneity (statistics); Sample size determination; Parametric statistics; Statistics; Econometrics; Statistical hypothesis testing; Parametric model; Computer science; Limiting; Simple (philosophy); Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.122513,0.001129619,0.002821149,0.005853478,0.001505484,0.002117607,0.004495903,0.003295247,0.01297967],"category_scores_gemma":[0.547668,0.0008687674,0.002628323,0.003688473,0.006741151,0.00482313,0.004642482,0.004851985,0.0009526777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002539437,"about_ca_system_score_gemma":0.00212668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001211628,"about_ca_topic_score_gemma":0.0009353078,"domain_scores_codex":[0.8666044,0.1089098,0.004357483,0.0075048,0.01171261,0.0009109454],"domain_scores_gemma":[0.4015532,0.5586596,0.007969846,0.02146686,0.008970932,0.001379652],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002106002,0.0003615938,0.02098479,0.001476335,0.001870733,0.0007745661,0.001567661,0.04273763,0.003370889,0.5690425,0.01229491,0.3434125],"study_design_scores_gemma":[0.001524798,0.001811701,0.01433789,0.0010495,0.0006313272,0.0009910481,0.0005651557,0.2420989,0.007513026,0.7135935,0.01566819,0.0002149326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02485446,0.0007883697,0.9660438,0.0009837059,0.0003205384,0.001628886,0.0002874548,0.0003567699,0.004736014],"genre_scores_gemma":[0.4229165,0.0004184605,0.5641283,0.0008374517,0.000487555,0.008949167,0.0006543992,0.0003301276,0.001278021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.877487,"threshold_uncertainty_score":0.6479185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937313448136663,"score_gpt":0.2753395663055425,"score_spread":0.2459664318241758,"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."}}