{"id":"https://openalex.org/W2387530501","doi":"https://doi.org/10.1145/2939672.2939775","title":"A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests","display_name":"A Multiple Test Correction for Streams and Cascades of Statistical Hypothesis Tests","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2387530501","doi":"https://doi.org/10.1145/2939672.2939775","mag":"2387530501"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2939775","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939775","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058054791","display_name":"Geoffrey I. Webb","orcid":"https://orcid.org/0000-0001-9963-5169"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Geoffrey I. Webb","raw_affiliation_strings":["Monash University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005781957","display_name":"Fran\u00e7ois Petitjean","orcid":"https://orcid.org/0000-0001-5334-3574"},"institutions":[{"id":"https://openalex.org/I56590836","display_name":"Monash University","ror":"https://ror.org/02bfwt286","country_code":"AU","type":"education","lineage":["https://openalex.org/I56590836"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Fran\u00e7ois Petitjean","raw_affiliation_strings":["Monash University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Monash University, Melbourne, Australia","institution_ids":["https://openalex.org/I56590836"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I56590836"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1255","last_page":"1264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9941999912261963,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9941999912261963,"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/T10136","display_name":"Statistical Methods and Inference","score":0.991599977016449,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/false-discovery-rate","display_name":"False discovery rate","score":0.765735387802124},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.7444089651107788},{"id":"https://openalex.org/keywords/multiple-comparisons-problem","display_name":"Multiple comparisons problem","score":0.7047609090805054},{"id":"https://openalex.org/keywords/null-hypothesis","display_name":"Null hypothesis","score":0.6933879852294922},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6443409323692322},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.602066159248352},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5770668983459473},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5012147426605225},{"id":"https://openalex.org/keywords/null","display_name":"Null (SQL)","score":0.41565656661987305},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41208162903785706},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3872908353805542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38131269812583923},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35755330324172974},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.31842076778411865}],"concepts":[{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.765735387802124},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.7444089651107788},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.7047609090805054},{"id":"https://openalex.org/C191988596","wikidata":"https://www.wikidata.org/wiki/Q628374","display_name":"Null hypothesis","level":2,"score":0.6933879852294922},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6443409323692322},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.602066159248352},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5770668983459473},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5012147426605225},{"id":"https://openalex.org/C203763787","wikidata":"https://www.wikidata.org/wiki/Q371029","display_name":"Null (SQL)","level":2,"score":0.41565656661987305},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41208162903785706},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3872908353805542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38131269812583923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35755330324172974},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31842076778411865},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2939672.2939775","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939775","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W113002869","https://openalex.org/W1524326598","https://openalex.org/W1835942292","https://openalex.org/W1967030981","https://openalex.org/W1970588407","https://openalex.org/W1993686588","https://openalex.org/W1993824876","https://openalex.org/W1998632536","https://openalex.org/W2012150214","https://openalex.org/W2030764200","https://openalex.org/W2034562813","https://openalex.org/W2038483783","https://openalex.org/W2054658115","https://openalex.org/W2096066120","https://openalex.org/W2100236179","https://openalex.org/W2104579579","https://openalex.org/W2110065044","https://openalex.org/W2110381504","https://openalex.org/W2110613580","https://openalex.org/W2119008358","https://openalex.org/W2120659848","https://openalex.org/W2121044470","https://openalex.org/W2123896286","https://openalex.org/W2133420109","https://openalex.org/W2142390772","https://openalex.org/W2153699776","https://openalex.org/W2155573334","https://openalex.org/W2159123127","https://openalex.org/W2162154276","https://openalex.org/W2394735783","https://openalex.org/W2542081672","https://openalex.org/W2596585349","https://openalex.org/W2949581777","https://openalex.org/W2951936974","https://openalex.org/W2990138404","https://openalex.org/W3006419771","https://openalex.org/W3047398590","https://openalex.org/W3121452939","https://openalex.org/W4235234743","https://openalex.org/W4240080800","https://openalex.org/W4251459653","https://openalex.org/W4256669726","https://openalex.org/W4300121803","https://openalex.org/W4300945981","https://openalex.org/W4302423442","https://openalex.org/W6631500773","https://openalex.org/W6659268071","https://openalex.org/W6677876135","https://openalex.org/W6729092535"],"related_works":["https://openalex.org/W200550325","https://openalex.org/W3174861742","https://openalex.org/W2046875666","https://openalex.org/W2169611148","https://openalex.org/W2317297514","https://openalex.org/W3122337281","https://openalex.org/W3013502713","https://openalex.org/W2152678181","https://openalex.org/W2627831963","https://openalex.org/W4301749514"],"abstract_inverted_index":{"Statistical":[0],"hypothesis":[1,33,63],"testing":[2,96,119],"is":[3,20,40,81,87],"a":[4,22,27,31,90],"popular":[5],"and":[6,84],"powerful":[7],"tool":[8],"for":[9,100,120],"inferring":[10],"knowledge":[11],"from":[12],"data.":[13],"For":[14],"every":[15],"such":[16,127],"test":[17],"performed,":[18,67],"there":[19],"always":[21],"non-zero":[23],"probability":[24,42],"of":[25,43,62,79,111,124,135,145],"making":[26,44],"false":[28,48],"discovery,":[29],"i.e.~rejecting":[30],"null":[32],"in":[34,103,150],"error.":[35],"Familywise":[36],"error":[37,72],"rate":[38],"(FWER)":[39],"the":[41,60,77,85,94,109,133,143],"at":[45],"least":[46],"one":[47],"discovery":[49],"during":[50],"an":[51,71],"inference":[52],"process.":[53],"The":[54],"expected":[55],"FWER":[56,102],"grows":[57],"exponentially":[58],"with":[59],"number":[61,78,144],"tests":[64,80],"that":[65,70,108,137],"are":[66],"almost":[68],"guaranteeing":[69],"will":[73,138],"be":[74,113,139,148],"committed":[75],"if":[76],"big":[82],"enough":[83],"risk":[86],"not":[88],"managed;":[89],"problem":[91],"known":[92,149],"as":[93,128],"multiple":[95,104],"problem.":[97],"State-of-the-art":[98],"methods":[99],"controlling":[101],"comparison":[105],"settings":[106],"require":[107],"set":[110,134],"hypotheses":[112,136],"predetermined.":[114],"This":[115],"greatly":[116],"hinders":[117],"statistical":[118,125],"many":[121],"modern":[122],"applications":[123],"inference,":[126],"model":[129],"selection,":[130],"because":[131],"neither":[132],"tested,":[140],"nor":[141],"even":[142],"hypotheses,":[146],"can":[147],"advance.":[151]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}