Use of the p-values as a size-dependent function to address practical differences when analyzing large datasets.

Area of Science:

• Biostatistics
• Bioinformatics
• Biomedical Research

Background:

• P-values are standard in biomedical research for data-driven decisions using null hypothesis significance testing.
• Traditional p-value interpretation faces challenges with large datasets, where increased sample size often leads to null hypothesis rejection.
• This limitation can obscure meaningful biological differences in extensive datasets.

Purpose of the Study:

• To propose a novel approach for detecting differences in large datasets that overcomes p-value limitations.
• To introduce new descriptive parameters that account for sample size effects in p-value interpretation.
• To reduce uncertainty in identifying biological differences between compared experiments.

Main Methods:

• Developed a new methodology focusing on the relationship between p-values and sample size.
• Introduced novel descriptive parameters to adjust p-value interpretation for large datasets.
• Validated the approach using simulated and experimental data, including graphical and quantitative characterization.

Main Results:

• The proposed method effectively characterizes differences between experiments in large datasets.
• New parameters provide a more reliable interpretation of statistical significance, independent of sample size inflation.
• The methodology aids researchers in making more informed decisions about biological differences.

Conclusions:

• The novel approach enhances the interpretation of statistical significance in large-scale biomedical research.
• It offers a robust framework for detecting true biological differences, mitigating issues with sample size.
• This methodology improves decision-making processes for researchers analyzing extensive datasets.