{"id":"https://openalex.org/W7217154362","doi":null,"title":"Problems with Probabilistic Hindsight: A Comparison of Methods for Retrospective Statistical Power Analysis","display_name":"Problems with Probabilistic Hindsight: A Comparison of Methods for Retrospective Statistical Power Analysis","publication_year":2000,"publication_date":"2000-08-01","ids":{"openalex":"https://openalex.org/W7217154362"},"language":"en","primary_location":{"id":"pmh:oai:ojs-dev.lib.ua.edu:article/185","doi":null,"is_oa":true,"landing_page_url":"https://ojs.lib.ua.edu/index/article/view/185","pdf_url":null,"source":{"id":"https://openalex.org/S4210197319","display_name":"General Linear Model Journal","issn_l":"2379-0091","issn":["2379-0091","2379-0105"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2379-0091","raw_type":"info:eu-repo/semantics/article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ojs.lib.ua.edu/index/article/view/185","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5156144781","display_name":"Jeffrey Kromrey","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kromrey, Jeffrey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5002123737","display_name":"Kristine Y. Hogarty","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hogarty, Kristine Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.59114315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.26420000195503235,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.26420000195503235,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10206","display_name":"Meta-analysis and systematic reviews","score":0.24779999256134033,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T14374","display_name":"Statistical Methods in Epidemiology","score":0.21899999678134918,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/statistical-power","display_name":"statistical power","score":0.8029999732971191},{"id":"https://openalex.org/keywords/sampling-error","display_name":"sampling error","score":0.7139999866485596},{"id":"https://openalex.org/keywords/median-unbiased-estimators","display_name":"median unbiased estimator","score":0.7549999952316284},{"id":"https://openalex.org/keywords/plug-in-estimator","display_name":"plug-in estimator","score":0.9449999928474426},{"id":"https://openalex.org/keywords/interval-estimation","display_name":"interval estimation","score":0.7879999876022339},{"id":"https://openalex.org/keywords/effect-size","display_name":"effect size","score":0.7749999761581421},{"id":"https://openalex.org/keywords/hypothesis-testing","display_name":"hypothesis testing","score":0.6629999876022339}],"concepts":[{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.7861999869346619},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.7073000073432922},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6909000277519226},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.5982999801635742},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.5848000049591064},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.49559998512268066},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43529999256134033},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41659998893737793},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4124999940395355},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4090000092983246},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C205167067","wikidata":"https://www.wikidata.org/wiki/Q3300636","display_name":"Interval estimation","level":3,"score":0.39419999718666077},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37389999628067017},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3693000078201294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35370001196861267},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3467000126838684},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.32089999318122864},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2535000145435333},{"id":"https://openalex.org/C32809988","wikidata":"https://www.wikidata.org/wiki/Q1071004","display_name":"Chi-square test","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:ojs-dev.lib.ua.edu:article/185","doi":null,"is_oa":true,"landing_page_url":"https://ojs.lib.ua.edu/index/article/view/185","pdf_url":null,"source":{"id":"https://openalex.org/S4210197319","display_name":"General Linear Model Journal","issn_l":"2379-0091","issn":["2379-0091","2379-0105"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2379-0091","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:ojs-dev.lib.ua.edu:article/185","doi":null,"is_oa":true,"landing_page_url":"https://ojs.lib.ua.edu/index/article/view/185","pdf_url":null,"source":{"id":"https://openalex.org/S4210197319","display_name":"General Linear Model Journal","issn_l":"2379-0091","issn":["2379-0091","2379-0105"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2379-0091","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7096419010","https://openalex.org/W2000358932","https://openalex.org/W7095397228","https://openalex.org/W2131702527","https://openalex.org/W2460347071","https://openalex.org/W2065532278","https://openalex.org/W7098451747","https://openalex.org/W4250829163","https://openalex.org/W2099200313","https://openalex.org/W7110661609"],"abstract_inverted_index":{"In":[0],"contrast":[1],"to":[2,34,64,115],"prospective":[3],"uses":[4],"of":[5,14,18,30,42,46,54,69,88,98,145,151,157,176],"power":[6,9,17,48,91,139],"analysis,":[7],"retrospective":[8,47,90],"analysis":[10],"provides":[11],"an":[12,23,55],"estimate":[13,57],"the":[15,37,50,89,99,105,116,137,143,158,174,177],"statistical":[16,138],"a":[19,66],"hypothesis":[20],"test":[21],"after":[22],"investigation":[24],"has":[25],"been":[26],"conducted.":[27],"The":[28,83,161],"purpose":[29],"this":[31],"research":[32,70],"was":[33],"empirically":[35],"investigate":[36,65],"bias":[38,127],"and":[39,49,72,107,189],"sampling":[40,152],"errors":[41],"three":[43,156],"point":[44,103,159],"estimators":[45,110],"confidence":[51,166],"band":[52,167],"coverage":[53,168],"interval":[56,162],"approach.":[58],"Monte":[59],"Carlo":[60],"methods":[61],"were":[62,94,122],"used":[63],"broad":[67],"range":[68],"designs":[71],"population":[73],"effect":[74,134,191],"sizes":[75,132,135],"that":[76,86,180],"may":[77],"be":[78],"encountered":[79],"in":[80],"field":[81],"research.":[82],"results":[84],"suggest":[85],"none":[87],"estimation":[92],"techniques":[93],"effective":[95],"across":[96],"all":[97,155],"conditions":[100,171],"examined.":[101],"For":[102],"estimates,":[104],"\u201cunbiased\u201d":[106],"\u201cmedian":[108],"unbiased\u201d":[109],"showed":[111,164],"improved":[112],"performance":[113],"relative":[114],"plug-in":[117],"estimator,":[118],"but":[119,173],"these":[120,146],"procedures":[121],"not":[123],"completely":[124],"free":[125],"from":[126],"except":[128,185],"under":[129,169],"large":[130,149,187,190],"sample":[131,188],"andlarge":[133],"(as":[136],"approaches":[140],"unity).":[141],"Further":[142],"RMSE":[144],"estimates":[147,163],"suggests":[148,179],"amounts":[150],"error":[153],"for":[154,186],"estimators.":[160],"good":[165],"most":[170],"examined,":[172],"width":[175],"bands":[178],"they":[181],"are":[182],"relatively":[183],"uninformative":[184],"size":[192],"conditions.":[193]},"counts_by_year":[],"updated_date":"2026-10-04T09:36:47.201465","created_date":"2026-10-03T00:00:00"}