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Review
. 2024 Jun 26;17(7):sfae197.
doi: 10.1093/ckj/sfae197. eCollection 2024 Jul.

Common mistakes in biostatistics

Graziella D'Arrigo  1 , Samar Abd El Hafeez  2 , Sabrina Mezzatesta  1 , Domenico Abelardo  3 , Fabio Pasquale Provenzano  1 , Antonio Vilasi  1 , Claudia Torino  1 , Giovanni Tripepi  1
Affiliations
Review

Common mistakes in biostatistics

Graziella D'Arrigo et al. Clin Kidney J. .

Abstract

Biostatistics plays a pivotal role in developing, interpreting and drawing conclusions from clinical, biological and epidemiological data. However, the improper application of statistical methods can lead to erroneous conclusions and misinterpretations. This paper provides a comprehensive examination of the most frequent mistakes encountered in the biostatistical analysis process. We identified and elucidated 10 common errors in biostatistical analysis. These include using the wrong metric to describe data, misinterpreting P-values, misinterpreting the 95% confidence interval, misinterpreting the hazard ratio as an index of prognostic accuracy, ignoring the sample size calculation, misinterpreting analysis by strata in randomized clinical trials, confusing correlation and causation, misunderstanding confounders and mediators, inadequately codifying variables during the data collection, and bias arising when group membership is attributed on the basis of future exposure in retrospective studies. We discuss the implications of these errors and propose some practical strategies to mitigate their impact. By raising awareness of these pitfalls, this paper aims to enhance the rigor and reproducibility of biostatistical analyses, thereby fostering more robust and reliable biomedical research findings.

Keywords: methodological errors; mistakes in biostatistics; mistakes in clinical epidemiology.

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Conflict of interest statement

None declared.

Figures

Figure 1:
Figure 1:
Description of the incidence rate (and IRR) calculation in two hypothetical groups of patients (see text for more detail).
Figure 2:
Figure 2:
Explanation of the basic concepts underlying the sample size calculation (see text for more detail). The main reasons for the sample size calculation are also listed.
Figure 3:
Figure 3:
Graphical description of the immortal time bias (see text for more detail).
See this image and copyright information in PMC

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