Missing data is far more common in RCTs and systematic reviews than most readers realize, and its impact on treatment estimates can be substantial. Dropouts, missed visits, and unmeasured outcomes weaken the balance created by randomization, reduce power, and can bias results in either direction. Because missingness is often not random, trialists must understand what the data type is and choose appropriate methods—such as multiple imputation, maximum likelihood, or structured sensitivity analyses—to test the robustness of findings. Systematic reviews face similar challenges and should assess how each study handled missingness, evaluate risk of bias, and explore best- and worst-case assumptions. Ultimately, the most effective strategy remains prevention through thoughtful trial design and proactive follow-up.