Bootstrap Method as a Tool for Analyzing Data with Atypical Distributions Deviating from Parametric Assumptions: Critique and Effectiveness Evaluation

Joanna Kostanek (), Kamil Karolczak, Wiktor Kuliczkowski and Cezary Watala ()
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Joanna Kostanek: Department of Haemostatic Disorders, Medical University of Lodz, 6/8 Mazowiecka Street, 92-215 Lodz, Poland
Kamil Karolczak: Department of Haemostatic Disorders, Medical University of Lodz, 6/8 Mazowiecka Street, 92-215 Lodz, Poland
Wiktor Kuliczkowski: Institute for Heart Diseases, Wroclaw Medical University, 213 Borowska Street, 50-556 Wroclaw, Poland
Cezary Watala: Department of Haemostatic Disorders, Medical University of Lodz, 6/8 Mazowiecka Street, 92-215 Lodz, Poland

Data, 2024, vol. 9, issue 8, 1-20

Abstract: In today’s research environment characterized by exponential data growth and increasing complexity, the selection of appropriate statistical tests, tailored to research objectives and data distributions, is paramount for rigorous analysis and accurate interpretation. This article explores the growing prominence of bootstrapping, an advanced statistical technique for multiple comparisons analysis, offering flexibility and customization by estimating sample distributions without assuming population distributions, thus serving as a valuable alternative to traditional methods in various data scenarios. Computer simulations were conducted using data from cardiovascular disease patients. Two approaches, spontaneous partly controlled simulation and fully constrained simulation using self-written R scripts, were utilized to generate datasets with specified distributions and analyze the data using tests for comparing more than two groups. The utilization of the bootstrap method greatly improves statistical analysis, especially in overcoming the constraints of conventional parametric tests. Our research showcased its effectiveness in comparing multiple scenarios, yielding strong findings across diverse distributions, even with minor inflation in p values. Serving as a valuable substitute for parametric approaches, bootstrap promotes careful consideration when rejecting hypotheses, thus fostering a deeper understanding of statistical nuances and bolstering analytical rigor.

Keywords: bootstrap; resampling procedures; violation of parametric tests assumptions; multiple comparisons; simulations (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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