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PRODID:-//pretalx//cfp.pydata.org//pydataglobal2025//speaker//ECV9N3
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UID:pretalx-pydataglobal2025-YHTMZY@cfp.pydata.org
DTSTART:20251209T193000Z
DTEND:20251209T201000Z
DESCRIPTION:Why do male test takers consistently score about 30 points high
 er than female test takers on the mathematics section of the SAT? Does thi
 s reflect an actual difference in math ability\, or is it an artifact of s
 election bias—if young men with low math ability are less likely to take
  the test than young women with the same ability?\n\nThis talk presents a 
 Bayesian model that estimates how much of the observed difference can be e
 xplained by selection effects. We’ll walk through a complete Bayesian wo
 rkflow\, including prior elicitation with PreliZ\, model building in PyMC\
 , and validation with ArviZ\, showing how Bayesian methods disentangle lat
 ent traits from observed outcomes and separate the signal from the noise.\
 n\nNo prior knowledge of Bayesian statistics is required\; attendees shoul
 d be familiar with Python and common probability distributions.
DTSTAMP:20260612T024542Z
LOCATION:Live from PyData Boston
SUMMARY:The SAT math gap: gender difference or selection bias? - Allen  Dow
 ney
URL:https://cfp.pydata.org/pydataglobal2025/talk/YHTMZY/
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