This paper explores the relationship between uncertainty and the Markowitz asset allocation framework, focusing on how parameter estimation errors affect out-of-sample performance. It investigates how uncertainty impacts the effectiveness of Markowitz strategies and compares them with alternative allocation methods. The study highlights that incorporating uncertainty management can enhance the Markowitz framework, challenging the assumption that longer sample periods always yield better results. It also shows that short-sale constraints can improve portfolio effectiveness. The research emphasizes the practical implications of parameter uncertainty on asset allocation outcomes and provides insights for investors, portfolio managers, and financial practitioners to refine their strategies. The paper uses a rigorous out-of-sample evaluation to assess the performance of various asset allocation approaches, including the equally weighted allocation, which demonstrates surprising resilience. The study also explores the impact of data abundance, the reliability of performance predictions, and the influence of allocation constraints. The findings suggest that alternative strategies, such as the "1 over N" approach, can outperform traditional Markowitz models in certain scenarios. The paper concludes that addressing uncertainty is crucial for improving real-world investment decisions and that the Markowitz framework, while theoretically sound, requires adaptation to better handle practical challenges. The research contributes to the ongoing discussion on effective strategies for navigating uncertainty in asset allocation.This paper explores the relationship between uncertainty and the Markowitz asset allocation framework, focusing on how parameter estimation errors affect out-of-sample performance. It investigates how uncertainty impacts the effectiveness of Markowitz strategies and compares them with alternative allocation methods. The study highlights that incorporating uncertainty management can enhance the Markowitz framework, challenging the assumption that longer sample periods always yield better results. It also shows that short-sale constraints can improve portfolio effectiveness. The research emphasizes the practical implications of parameter uncertainty on asset allocation outcomes and provides insights for investors, portfolio managers, and financial practitioners to refine their strategies. The paper uses a rigorous out-of-sample evaluation to assess the performance of various asset allocation approaches, including the equally weighted allocation, which demonstrates surprising resilience. The study also explores the impact of data abundance, the reliability of performance predictions, and the influence of allocation constraints. The findings suggest that alternative strategies, such as the "1 over N" approach, can outperform traditional Markowitz models in certain scenarios. The paper concludes that addressing uncertainty is crucial for improving real-world investment decisions and that the Markowitz framework, while theoretically sound, requires adaptation to better handle practical challenges. The research contributes to the ongoing discussion on effective strategies for navigating uncertainty in asset allocation.