{"id":"W2550638838","doi":"10.1146/annurev-statistics-060116-054139","title":"<i>p</i>-Values: The Insight to Modern Statistical Inference","year":2016,"lang":"en","type":"article","venue":"Annual Review of Statistics and Its Application","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Scalar (mathematics); Mathematics; Inference; Applied mathematics; Statistical inference; Range (aeronautics); Exponential function; Statistical model; Computer science; Statistics; Mathematical analysis; Artificial intelligence","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":[0.03639691,0.001722372,0.00231747,0.004503773,0.0009178848,0.005480348,0.003550145,0.004023963,0.003359173],"category_scores_gemma":[0.1474777,0.001065154,0.002162541,0.004496994,0.01645182,0.008742078,0.003696179,0.01225899,0.002134979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001951221,"about_ca_system_score_gemma":0.002341126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001064471,"about_ca_topic_score_gemma":0.000458738,"domain_scores_codex":[0.9769754,0.01767723,0.00094274,0.001589931,0.00249485,0.0003197024],"domain_scores_gemma":[0.8574685,0.1283369,0.003195497,0.007614403,0.002861912,0.0005226432],"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":[0.00003558442,0.00002465486,0.0006252434,0.0003434959,0.00009238572,0.0001778587,0.0002337636,0.00474597,0.0002394407,0.9295266,0.01197072,0.05198421],"study_design_scores_gemma":[0.000009105711,0.0000329113,0.0001825467,0.0001507942,0.00001781595,0.0001701378,0.0000287781,0.01124069,0.000202636,0.9736549,0.01429014,0.00001954236],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0009033666,0.008200508,0.9755961,0.009896135,0.0008019215,0.00003241497,0.0001390362,0.0002231935,0.004207429],"genre_scores_gemma":[0.1100361,0.01970242,0.8408565,0.01294017,0.009619064,0.0008223078,0.0003453268,0.0008503422,0.004827745],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.9636031,"threshold_uncertainty_score":0.1924875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2291872055001569,"score_gpt":0.5231912735818536,"score_spread":0.2940040680816967,"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."}}