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[Pitfalls in the statistical world].

C Kiese-Himmel1, S K Plontke2

  • 1Phoniatrisch/Pädaudiologische Psychologie, Universitätsmedizin Göttingen, Waldweg 35, 37073, Göttingen, Deutschland. ckiese@med.uni-goettingen.de.

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|September 11, 2019
PubMed
Summary
This summary is machine-generated.

Researchers must be aware of methodological issues in data handling to prevent errors. This awareness is crucial for accurate scientific reporting and reliable healthcare practices, avoiding false positive results.

Keywords:
False positive findingsHARKingPreregistrationReplicabilityp-hacking

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Area of Science:

  • Biostatistics
  • Research Methodology
  • Scientific Integrity

Background:

  • Methodological errors in data acquisition, evaluation, analysis, and reporting are common due to a lack of awareness.
  • These errors can significantly impact the interpretation of scientific findings and subsequently affect clinical practice and healthcare outcomes.
  • A critical need exists to address these issues for more reliable scientific evidence.

Purpose of the Study:

  • To enhance awareness regarding the responsible handling of study data in scientific research.
  • To highlight common statistical pitfalls encountered in empirical research that can lead to erroneous conclusions.
  • To provide strategies for avoiding questionable or incorrect research practices and mitigating risks of false positives.

Main Methods:

  • Review of common statistical pitfalls in empirical research.
  • Illustrative examples of data handling errors.
  • Discussion of best practices for data integrity and analysis.

Main Results:

  • Identified frequent mistakes in data acquisition, evaluation, analysis, and reporting.
  • Demonstrated how statistical pitfalls increase the likelihood of false positive results.
  • Outlined methods to avoid these risks and ensure data reliability.

Conclusions:

  • Awareness of methodological problems is key to preventing data errors in scientific studies.
  • Addressing statistical pitfalls is essential for improving the quality of research and its impact on healthcare.
  • Responsible data handling practices are vital for maintaining scientific integrity and trustworthy clinical practice.