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Statistical practices: the seven deadly sins.

Scott Millis1

  • 1Kessler Medical Rehabilitation Research and Education Corporation, West Orange, NJ 07052, USA. smillis@kmrrec.org

Child Neuropsychology : a Journal on Normal and Abnormal Development in Childhood and Adolescence
|September 19, 2003
PubMed
Summary
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This study highlights common statistical pitfalls in child neuropsychology research, such as over-reliance on null hypothesis testing and ignoring missing data. It offers solutions to improve applied statistical analysis and reporting in manuscripts.

Area of Science:

  • Child Neuropsychology
  • Applied Statistical Analysis

Background:

  • Common issues in applied statistical analysis can compromise research integrity.
  • Child neuropsychological research often faces specific statistical challenges.

Purpose of the Study:

  • To identify and discuss prevalent statistical problems in child neuropsychological research.
  • To provide guidelines for addressing these issues in manuscript preparation.

Main Methods:

  • Review and discussion of common statistical problems.
  • Formulation of recommendations for applied statistical analysis.

Main Results:

  • Identified issues include over-reliance on null hypothesis statistical testing, inadequate power analysis, inappropriate use of asymptotic approximations, handling of missing data, multiplicity problem, stepwise regression, and lack of model diagnostics.

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  • Specific suggestions are provided for each identified problem.
  • Conclusions:

    • Addressing these statistical issues is crucial for enhancing the quality and reliability of child neuropsychological research.
    • Adherence to proposed guidelines can improve the rigor of statistical reporting and interpretation.