Background: Resampling methods such as the Monte Carlo (MC) and Bootstrap Approach (BA) are very flexible tools for statistical inference. They are used in general in experiments with small sample size or where the parametric test assumptions are not met. They are also used in situations where expressions for properties of complex estimators are statistically intractable. However, the MC and BA methods require relatively large random samples to estimate the parameters of the full permutation (FP) or exact distribution. Objective: The objective of this research study was to develop an efficient statistical computational resampling method that compares two population parameters, using a balanced and controlled sampling design. The application of the new method, the balanced randomization (BR) method, is discussed using microarray data where sample sizes are generally small. Methods: Multiple datasets were simulated from real data to compare the accuracy and efficiency of the methods (BR, MC, and BA). Datasets, probability distributions, parameters, and sample sizes were varied in the simulation. The correlation between the exact p-value and the p-values generated by simulation provide a measure of accuracy/consistency to compare methods. Sensitivity, specificity, power function, false negative and positive rates using graphical and multivariate analyses were used to compare methods. Results and Discussions: The correlation between the exact p-value and those estimated from simulation are higher for BR and MC, (increasing somewhat with increasing sample size), much less for BA, and most pronounced for skewed distributions (lognormal, exponential). Furthermore, the relative proportion of 95%/99% CI containing the true p-value for BR vs. MC=3%/1.3% (p<0.0001) and BR vs. BA=20%/15% (p<0.0001). The sensitivity, specificity and power function of the BR method were shown to have a slight advantage compared to those of MC and BA in most situations. As an example, the BR method was applied to a microarray study to discuss significantly differentially expressed genes. / acase@tulane.edu