BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.com//sips2025-budapest//speaker//WJTCGE
BEGIN:VTIMEZONE
TZID:CET
BEGIN:STANDARD
DTSTART:20001029T040000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-sips2025-budapest-EQQW99@pretalx.com
DTSTART;TZID=CET:20250625T150700
DTEND;TZID=CET:20250625T151400
DESCRIPTION:Verifying the analytical reproducibility of research findings r
 equires access to the raw data\, however this is often not possible due to
  ethical or practical reasons. That being said\, one can sometimes still c
 heck the consistency of a set of reported numbers. For example\, based on 
 the test statistic and degrees of freedom\, one can calculate the correspo
 nding p-value and check whether it matches with the reported value. The St
 atcheck application (Nuijten & Epskamp\, 2024) automates this procedure fo
 r any text uploaded by the user. However\, results that do not exactly mat
 ch the intended APA format are not recognized by Statcheck\, and neither a
 re corrections for multiple testing or assumption violations. The present 
 study used AI to examine whether the extraction and subsequent verificatio
 n of such tests can be improved. Preliminary findings using the gpt-4o-min
 i model on a set of manually coded papers\, suggest marked improvements co
 mpared to Statcheck.
DTSTAMP:20260514T165759Z
LOCATION:Second floor 214
SUMMARY:LT18: Improving reproducibility through AI-powered data extraction:
  A case study involving Statcheck - Tom Heyman\, Stijn Pleunes
URL:https://pretalx.com/sips2025-budapest/talk/EQQW99/
END:VEVENT
END:VCALENDAR