{"id":"W2945261981","doi":"10.2427/13059","title":"Continuity correction of Pearson’s chi-square test in 2x2 Contingency Tables: A mini-review on recent development","year":2022,"lang":"en","type":"article","venue":"Epidemiology Biostatistics and Public Health","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Contingency table; Pearson's chi-squared test; Chi-square test; Pearson product-moment correlation coefficient; Statistic; Nonparametric statistics; Continuity correction; Statistics; Test (biology); Mathematics; Test statistic; Square (algebra); Statistical hypothesis testing; Calculus (dental); Poisson distribution; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008120596,0.0001597614,0.0008114773,0.00004494925,0.0003555707,0.000009037511,0.0001468645,0.00005979431,0.0009907235],"category_scores_gemma":[0.012723,0.00007773057,0.00004702228,0.0005363461,0.00009949162,0.00002163597,0.00008619743,0.0003184102,0.000002702868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001091696,"about_ca_system_score_gemma":0.0001087422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001120704,"about_ca_topic_score_gemma":0.001641618,"domain_scores_codex":[0.9951042,0.002733423,0.001075892,0.0004188211,0.0001784764,0.0004891753],"domain_scores_gemma":[0.9919119,0.007133505,0.0005445993,0.00007412451,0.0001031722,0.0002326539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001860676,0.0003435942,0.1282653,0.00013869,0.00001626658,0.000003462775,0.0002000804,0.000003923031,0.00004275806,0.004982521,0.009885615,0.8560992],"study_design_scores_gemma":[0.0001677642,0.001030343,0.655396,0.0001432357,0.000008671715,0.000008748433,0.0006905159,0.000904425,0.000003956483,0.0006501889,0.3408228,0.0001733309],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7755504,0.04671243,0.007408422,0.1561108,0.002441749,0.003439265,0.005647251,0.0001406231,0.002549041],"genre_scores_gemma":[0.917074,0.03219032,0.02237535,0.02616682,0.0001294545,0.0001790386,0.001539121,0.000004960978,0.0003410022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8559259,"threshold_uncertainty_score":0.9999225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1420862061615406,"score_gpt":0.3718474289774695,"score_spread":0.2297612228159289,"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."}}