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Related Experiment Videos

Simpson's Paradox: Examples.

Bokai Wang1, Pan Wu2, Brian Kwan3

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.

Shanghai Archives of Psychiatry
|May 9, 2018
PubMed
Summary
This summary is machine-generated.

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Simpson's paradox, a statistical anomaly, appears frequently across various fields. This paper demonstrates how this data interpretation inconsistency occurs in continuous, categorical, and time-to-event data.

Area of Science:

  • Statistics
  • Data Analysis

Background:

  • Simpson's paradox highlights discrepancies between conditional and marginal data interpretations.
  • This phenomenon is widespread across numerous scientific and analytical domains.

Purpose of the Study:

  • To illustrate the occurrence of Simpson's paradox.
  • To demonstrate its manifestation in diverse data types: continuous, categorical, and time-to-event.

Main Methods:

  • Exemplification through case studies.
  • Analysis of data across different formats.

Main Results:

  • Simpson's paradox was observed to be prevalent.
  • The paradox was shown to occur in continuous, categorical, and time-to-event data sets.
Keywords:
conditional expectationodd ratiotime-to-event analysis

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Conclusions:

  • Simpson's paradox is a common issue in data analysis.
  • Understanding its occurrence across data types is crucial for accurate interpretation.