Statistical data transformation in agrarian sciences for variance analysis: a systematic review

Area of Science:

• Agrarian Sciences
• Statistical Analysis

Background:

• Data transformation is a long-standing statistical technique used to improve the validity of variance analysis.
• It helps normalize error terms, correct variance heterogeneity, and ensure additive models for valid analysis of variances.
• Despite modern alternatives, data transformation remains prevalent in scientific literature.

Purpose of the Study:

• To systematically review the application of data transformations in agrarian sciences for variance analysis validation.
• To identify common transformations, reasons for their use, and potential errors or inconsistencies.
• To highlight shortcomings and misconceptions in the utilization of data transformation techniques.

Main Methods:

• Systematic literature review of studies in agrarian sciences employing data transformations.
• Analysis of transformation types, motivations for use, and application accuracy.
• Identification of errors and inconsistencies in published research.

Main Results:

• Identified specific data transformations commonly used in agrarian sciences.
• Observed frequent shortcomings and misconceptions in the application of these transformations.
• Found incomplete and inadequate utilization in a significant portion of the analyzed studies.

Conclusions:

• The application of data transformation in agrarian sciences often contains errors and inconsistencies.
• Misconceptions and inadequate utilization can lead to misguided and erroneous scientific conclusions.
• There is a need for greater accuracy and understanding when applying data transformation techniques in research.