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The p-value and model specification in statistics.

Bokai Wang1, Zhirou Zhou1, Hongyue Wang1

  • 1Departments of Biostatistics and Computational Biology and Anesthesiology, University of Rochester, Rochester, New York, USA.

General Psychiatry
|July 31, 2019
PubMed
Summary
This summary is machine-generated.

Misinterpreting p values in data analysis is common. Incorrect model specification can invalidate decisions based on p values, as their distribution may become inappropriate.

Keywords:
asymptotic distributionhypothesis testinglinear regression

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Area of Science:

  • Statistics
  • Data Analysis

Background:

  • The p value is a common metric for assessing statistical significance in data analysis.
  • Misuse and misinterpretation of p values are prevalent issues in scientific research.

Purpose of the Study:

  • To investigate the impact of incorrect model specification on the distribution and interpretation of p values.
  • To highlight the potential invalidity of statistical decisions when model assumptions are violated.

Main Methods:

  • The study likely involved simulations or theoretical analysis to examine p value distributions under various model misspecification scenarios.
  • Comparison of expected p value distributions with those obtained from misspecified models.

Main Results:

  • Results indicate that incorrect model specification significantly alters the true distribution of the p value.
  • This deviation from the expected distribution can lead to erroneous conclusions regarding statistical significance.

Conclusions:

  • Decisions based on p values are unreliable when the underlying statistical model is misspecified.
  • Researchers must ensure appropriate model specification to avoid invalid statistical inferences.