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An Experimental Analysis of Children's Ability to Provide a False Report about a Crime

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How To Lie with Statistics and Figures.

Jonathan Parks¹, D Dante Yeh²

¹Division of Trauma, Emergency Surgery, and Surgical Critical Care, Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.

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|July 16, 2021

Summary

Medical practitioners often misinterpret study results due to insufficient statistical training. This review highlights common statistical errors and reporting biases in medical literature to improve data interpretation.

Keywords:

bias experimental design spin statistical analysis

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Area of Science:

Medical Statistics
Scientific Communication
Research Methodology

Background:

Medical practitioners require strong statistical knowledge for research and clinical practice.
Lack of formal statistical training leads to common errors and misinterpretation of medical literature.
Awareness of statistical pitfalls is crucial for accurate application of new findings.

Purpose of the Study:

To review common statistical errors in medical literature reporting.
To identify and explain prevalent pitfalls in data presentation and interpretation.
To raise awareness of potential biases in scientific writing and study design.

Main Methods:

Review of common statistical errors in medical literature.
Analysis of misinterpretations of statistical significance and power.
Examination of biases in data presentation, study design, and scientific writing.

Main Results:

Common errors include incorrect average use, conflating statistical and practical significance, and misinterpreting statistical power.
Correlation is often falsely assumed to imply causation.
"Spin" in scientific writing and misguided design/reporting practices are prevalent.
Data presentation in graphs offers opportunities for bias introduction.

Conclusions:

Medical literature contains frequent statistical reporting errors and biases.
Increased awareness and formal training in statistics are needed for medical practitioners.
Recognizing these pitfalls is key to correcting unintentional bias and improving research accuracy.