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Patrick Breheny1, Arnold Stromberg2, Joshua Lambert3

  • 1Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA. patrick-breheny@uiowa.edu.

High-Throughput
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

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Analyzing p-value histograms helps identify experimental issues in biology and medicine. This study offers methods to interpret these histograms for diagnosing and resolving problems in scientific research.

Area of Science:

  • Statistics in Biology
  • Medical Research Methodology

Background:

  • Large-scale hypothesis testing is common in modern biology and medicine.
  • P-value histograms are a graphical tool for visualizing multiple test results.

Purpose of the Study:

  • To examine the shapes of p-value histograms and their implications.
  • To present inferential procedures for interpreting histogram shapes.
  • To diagnose and suggest remedies for experimental problems indicated by histogram patterns.

Main Methods:

  • Analysis of regular and irregular p-value histogram shapes.
  • Development of simple inferential procedures for shape interpretation.
  • Case studies using real biological experimental data.

Main Results:

Keywords:
diagnosticshistogramsinferencep-value

Related Experiment Videos

  • Identification of characteristic p-value histogram shapes associated with experimental issues.
  • Demonstration of how histogram patterns can signal specific problems.
  • Provision of practical examples from biological experiments.

Conclusions:

  • Interpreting p-value histogram shapes is a valuable diagnostic tool for researchers.
  • Understanding these shapes can lead to improved experimental design and data analysis.
  • The study provides a framework for identifying and addressing common problems in hypothesis testing.