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UID:pretalx-pyconde-pydata-2025-RQ8JBM@pretalx.com
DTSTART;TZID=CET:20250425T144000
DTEND;TZID=CET:20250425T151000
DESCRIPTION:A/B testing is a critical tool for making data-driven decisions
 \, yet its statistical underpinnings—p-values\, confidence intervals\, a
 nd hypothesis testing—are often challenging for those without a backgrou
 nd in statistics. Coders frequently encounter these concepts but lack a st
 raightforward way to compute and interpret them using their existing skill
  set.\nThis talk presents a practical approach to A/B test evaluations tai
 lored for coders. By utilizing Python’s random number generator and basi
 c loops\, it introduces bootstrapping as an accessible method for calculat
 ing p-values and confidence intervals directly from data. The goal is to s
 implify statistical concepts and provide coders with an intuitive understa
 nding of how to evaluate test results without relying on complex formulas 
 or statistical jargon.
DTSTAMP:20260415T100819Z
LOCATION:Ferrum
SUMMARY:Intuitive A/B Test Evaluations for Coders - Thomas Mayer
URL:https://pretalx.com/pyconde-pydata-2025/talk/RQ8JBM/
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