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Most PhD theses contain statistical errors, including low statistical power and incorrect test selection. Improving statistical training and mentorship is crucial for accurate research data interpretation.

Keywords:
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Area of Science:

  • Medical Research
  • Biostatistics

Background:

  • Accurate statistical analysis is vital for interpreting research data.
  • Errors in statistical methods are common in scientific publications and theses.
  • PhD students often utilize software like Excel and SPSS for data analysis.

Purpose of the Study:

  • To identify and analyze common statistical analysis mistakes in PhD theses.
  • To evaluate the quality of statistical methodology employed by PhD candidates.

Main Methods:

  • Cross-sectional analysis of a random sample of 15 PhD theses.
  • Theses were in the pre-approval stage at the Faculty of Medical Sciences, University of Kragujevac, Serbia.
  • Examination focused on statistical analysis of collected research data.

Main Results:

  • A high prevalence of statistical errors was observed, with 93% (14 out of 15) of theses containing at least one mistake.
  • Frequent errors included insufficient statistical power due to small sample sizes.
  • Inappropriate presentation of results in tables and graphs, and incorrect selection of statistical tests were also common.

Conclusions:

  • Statistical errors are prevalent in PhD theses, impacting data interpretation.
  • Enhancing statistical training programs for PhD students is recommended.
  • Increased mentor involvement in guiding statistical analysis is essential for improving research quality.