Empirical Distributions of the Robustified t-test Statistics

One sample t-test is commonly used for the statistical analysis of experimental data in comparing mean difference between matched pairs such as pretreatment and posttreatment assessments. It well known that sensitive to contamination occurs frequently practical applications. To overcome non-robustness t-test, Park (2018) developed two robustified analogues this test based on robust statistics provided asymptotic distributions statistics, assuming size large enough. These results may not be adequate making inference including hypothesis testing confidence interval when small or even moderate. The purpose paper conduct Monte Carlo simulations obtain empirical these their quantiles accurate inference. Useful tables are also constructed a quick finding with different sizes.