Release Party: npboottprm 0.1.0

Beta Release

The npboottprm 0.1.0 package implements nonparametric bootstrap tests with pooled resampling methods (NBPR), as detailed in Dwivedi, Mallawaarachchi, and Alvarado (2017). This release is a beta version of the software. The Shiny web app, its instruction manual, the open-source code base, and the CRAN package are accessible at https://www.mightymetrika.com/tools/npboottprm

When is npboottprm 0.1.0 Applicable in Research?

This package includes NBPR implementations for the independent t-test, paired t-test, and ANOVA F-test. Therefore, if your study aligns with any of these tests (or their alternatives like the Welch t-test, permutation t-test, etc.), you might find the npboottprm package useful.

A guiding principle is extracted from the conclusion of the Dwivedi et al. (2017) abstract: "We suggest using the nonparametric bootstrap test with pooled resampling method for comparing paired or unpaired means and for validating the one-way analysis of variance test results for non-normal data in small sample size studies."

Simulation Studies and Results

For a more in-depth assessment, researchers can turn to the simulation results highlighted in Dwivedi et al. (2017) or researchers can initiate a simulation comparing NBPR to standard methods on their own. While npboottprm can be combined with base R or the tidyverse to conduct simulation studies, this beta software version does not currently offer functions to simplify the process. Nevertheless, the simulation findings from Dwivedi et al. (2017) are illuminating. Tables II (focusing on type I error) and III (statistical power) present comparisons between the NBPR for the independent t-test and its traditional counterparts (i.e., Student’s t-test, Welch t-test, exact Wilcoxon rank sum test, permutation t-test) for sample sizes ranging from 3 to 15 across varying conditions where the distribution of the data and the variance between the two groups are: normal and equal variance; normal and unequal variance; same skewed and equal variance; same skewed and unequal variance; different skewed and equal variance; different skewed and unequal variance; unequal sample size, same skewed, and equal variance; Unequal sample size, same skewed, and unequal variance. In these tables, one of the NBPR's traditional counterparts tends to outperm the NBPR method.

Table IV delves into the comparison between NBPR and the traditional counterparts in terms of statistical power when dealing with non-normal data distributions such as log-normal,  Poisson, Chi-square, and Cauchy. In this table, the advantages of using NBPR become increasingly clear.

The subsequent sections of the paper elaborate on similar simulation findings for the paired t-test.

The simulations presented in these tables emphasize the conclusion previously drawn from the abstract which endorses the NBPR for comparing means (paired or unpaired) and for validating ANOVA results in small sample studies with non-normal data.

Reference

Dwivedi AK, Mallawaarachchi I, Alvarado LA (2017). “Analysis of small sample size studies using nonparametric bootstrap test with pooled resampling method.” Statistics in Medicine, 36 (14), 2187-2205.