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UID:pretalx-2025-WD9VWP@cfp.in.pycon.org
DTSTART;TZID=IST:20250913T105000
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DESCRIPTION:Statistics do not come intuitively to humans\; they always try 
 to find simple ways to describe complex things. Given a complex dataset\, 
 they may feel tempted to use simple summary statistics like the mean\, med
 ian\, or standard deviation to describe it. However\, these numbers are no
 t a replacement for visualizing the distribution.\n\nTo illustrate this fa
 ct\, researchers have generated many datasets that are very different visu
 ally\, but share the same summary statistics. In this talk\, I will discus
 s [Data Morph](https://github.com/stefmolin/data-morph)\, an open source p
 ackage that builds on previous research using simulated annealing to pertu
 rb an arbitrary input dataset into a variety of shapes\, while preserving 
 the mean\, standard deviation\, and correlation to multiple decimal points
 . I will showcase how it works\, discuss the challenges faced during devel
 opment\, and explore the limitations of this approach.
DTSTAMP:20260317T115929Z
LOCATION:Track 3
SUMMARY:Data Morph: A Cautionary Tale of Summary Statistics - Stefanie Moli
 n
URL:https://cfp.in.pycon.org/2025/talk/WD9VWP/
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