A multiple testing problem arises when two or more related null hypotheses are tested simultaneously, making the classical significance level unsuitable due to the risk of incorrectly rejecting multiple true null hypotheses. This work focuses on controlling the familywise error rate, which is the probability of rejecting at least one true null. The first major contribution is a comparison of multiple testing procedures based on their robustness properties, examining whether they maintain familywise error rate control across various data generating processes. A robust procedure also ensures reasonable power, with simulation results indicating that bootstrap methods enhance multiple testing procedures by better detecting the dependence structure of individual test statistics. The literature distinguishes between all-pairwise comparisons, multiple comparisons with a control (MCC), and multiple comparisons with the best (MCB). The second contribution applies robust multiple testing procedures to MCC issues, investigating whether emerging market funds outperform developed market funds and comparing several portfolio strategies derived from the Markowitz framework. These strategies include traditional sample-based, minimum-variance, shrinkage, and minimax approaches, evaluated using performance measures like the Sharpe ratio and Treynor ratio. The third contribution derives the asymptotic distribution of pairwise performance differe
