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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...
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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Does bad inference drive out good?

Marco Marozzi1

  • 1University of Calabria, Rende, Italy.

Clinical and Experimental Pharmacology & Physiology
|May 15, 2015
PubMed
Summary

Statistical errors are common in medical research. Nonparametric methods offer a solution when parametric assumptions fail, and journal statisticians can improve statistical rigor.

Area of Science:

  • Medical Statistics
  • Biostatistics
  • Scientific Publishing

Background:

  • The misuse of statistical methods is a significant concern in medical research.
  • A substantial percentage of published medical studies contain statistical errors.
  • Even high-impact journals frequently feature papers with flawed statistical reporting.

Purpose of the Study:

  • To address the ongoing debate regarding the appropriate application of statistics in medical literature.
  • To highlight the critical importance of assessing underlying assumptions in statistical analyses.
  • To propose solutions for improving the statistical quality of published medical research.

Main Methods:

  • Review of statistical practices in medical publications.
  • Analysis of the implications of unmet assumptions in parametric statistical methods.
Keywords:
case-control studyrandomnessrobust statistical methodsstatistical reporting

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  • Demonstration of nonparametric methods as viable alternatives.

    • Main Results:

    • Many medical papers, including those in top journals, lack careful assessment of statistical assumptions.
    • Nonparametric statistical methods are effective when parametric assumptions are violated.
    • The absence of rigorous statistical review contributes to the prevalence of errors.

    Conclusions:

    • Emphasizing the assessment of statistical assumptions is crucial for reliable medical research.
    • Nonparametric methods provide a robust alternative to parametric approaches when assumptions are not met.
    • Implementing dedicated statistical reviewers in journals can significantly enhance the quality and accuracy of statistical analyses in published medical papers.