{"id":"https://openalex.org/W2990372444","doi":"https://doi.org/10.1145/3313831.3376454","title":"How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results","display_name":"How Visualizing Inferential Uncertainty Can Mislead Readers About Treatment Effects in Scientific Results","publication_year":2020,"publication_date":"2020-04-21","ids":{"openalex":"https://openalex.org/W2990372444","doi":"https://doi.org/10.1145/3313831.3376454","mag":"2990372444"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3313831.3376454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","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/A5081429241","display_name":"Jake M. Hofman","orcid":"https://orcid.org/0000-0002-9364-9604"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4401726785","display_name":"Microsoft Research New York City (United States)","ror":"https://ror.org/056zprp28","country_code":null,"type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jake M. Hofman","raw_affiliation_strings":["Microsoft Research, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, New York, NY, USA","institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007746083","display_name":"Daniel G. Goldstein","orcid":"https://orcid.org/0000-0002-0970-5598"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4401726785","display_name":"Microsoft Research New York City (United States)","ror":"https://ror.org/056zprp28","country_code":null,"type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel G. Goldstein","raw_affiliation_strings":["Microsoft Research, New York, NY, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, New York, NY, USA","institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068008545","display_name":"Jessica Hullman","orcid":"https://orcid.org/0000-0001-6826-3550"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jessica Hullman","raw_affiliation_strings":["Northwestern University, Evanston, IL, USA"],"affiliations":[{"raw_affiliation_string":"Northwestern University, Evanston, IL, USA","institution_ids":["https://openalex.org/I111979921"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.946,"has_fulltext":false,"cited_by_count":66,"citation_normalized_percentile":{"value":0.999852,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9942,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9942,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10895","display_name":"Species Distribution and Climate Change","score":0.9831,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13283","display_name":"Mental Health Research Topics","score":0.9711,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/population-mean","display_name":"Population mean","score":0.5868873},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.5446791}],"concepts":[{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.70791626},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6339486},{"id":"https://openalex.org/C2993060064","wikidata":"https://www.wikidata.org/wiki/Q49918","display_name":"Population mean","level":3,"score":0.5868873},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5703306},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5617119},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.5446791},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.53984356},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4953219},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48727572},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.47586268},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44013178},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.42669296},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29460168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19378027},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.11485925},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3313831.3376454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":26,"referenced_works":["https://openalex.org/W1575252642","https://openalex.org/W1948497384","https://openalex.org/W1973727966","https://openalex.org/W1983075605","https://openalex.org/W2005534705","https://openalex.org/W2010759743","https://openalex.org/W2036002592","https://openalex.org/W2105824687","https://openalex.org/W2111308322","https://openalex.org/W2124702171","https://openalex.org/W2145680370","https://openalex.org/W2159445569","https://openalex.org/W2164410796","https://openalex.org/W2292312835","https://openalex.org/W2398344594","https://openalex.org/W2594087337","https://openalex.org/W2766518458","https://openalex.org/W2799246381","https://openalex.org/W2886512263","https://openalex.org/W2888554701","https://openalex.org/W2908972697","https://openalex.org/W2927052147","https://openalex.org/W2943497741","https://openalex.org/W3100035947","https://openalex.org/W4405635652","https://openalex.org/W563295823"],"related_works":["https://openalex.org/W4246117772","https://openalex.org/W4242857504","https://openalex.org/W3105429991","https://openalex.org/W3044342429","https://openalex.org/W2922142213","https://openalex.org/W2415989013","https://openalex.org/W2080004987","https://openalex.org/W2053654849","https://openalex.org/W1987643149","https://openalex.org/W129704017"],"abstract_inverted_index":{"When":[0],"presenting":[1],"visualizations":[2,105,109],"of":[3,19,28,46,88],"experimental":[4],"results,":[5],"scientists":[6],"often":[7],"choose":[8],"to":[9,64,80,96,107,141],"display":[10],"either":[11],"inferential":[12,137],"uncertainty":[13,15,25,138],"(e.g.,":[14,26],"in":[16,53,144],"the":[17,44,86],"estimate":[18],"a":[20,89],"population":[21],"mean)":[22,32],"or":[23,128],"outcome":[24,131],"variation":[27],"outcomes":[29],"around":[30],"that":[31,76,116,135],"about":[33,43],"their":[34],"estimates.":[35],"How":[36],"does":[37],"this":[38,51],"choice":[39],"impact":[40],"readers'":[41],"beliefs":[42],"size":[45],"treatment":[47,90],"effects?":[48],"We":[49,114],"investigate":[50],"question":[52],"two":[54],"experiments":[55],"comparing":[56],"95%":[57,65],"confidence":[58,93],"intervals":[59,67,94,127],"(means":[60,68],"and":[61,69,84,134],"standard":[62,70,108],"errors)":[63],"prediction":[66,97,126],"deviations).":[71],"The":[72,99],"first":[73],"experiment":[74,101],"finds":[75],"participants":[77,140],"are":[78],"willing":[79],"pay":[81],"more":[82],"for":[83,110],"overestimate":[85],"effect":[87,112],"when":[91],"shown":[92],"relative":[95],"intervals.":[98],"second":[100],"evaluates":[102],"how":[103],"alternative":[104],"compare":[106],"different":[111],"sizes.":[113],"find":[115],"axis":[117],"rescaling":[118],"reduces":[119],"error,":[120],"but":[121],"not":[122],"as":[123,125],"well":[124],"animated":[129],"hypothetical":[130],"plots":[132],"(HOPs),":[133],"depicting":[136],"causes":[139],"underestimate":[142],"variability":[143],"individual":[145],"outcomes.":[146]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2990372444","counts_by_year":[{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":19},{"year":2021,"cited_by_count":17},{"year":2020,"cited_by_count":9}],"updated_date":"2025-01-08T20:18:48.814270","created_date":"2019-12-05"}