Reproducibility of scientific experimentation has become a major concern, due to the perception that many published molecular studies cannot be replicated. Careful study design (based on statistical principles such as blocking, stratification, and randomization) has the potential to improve the quality of molecular data and the reproducibility of the scientific inference from the data. However, its use in practice has been scarce. We set out to demonstrate the logistic feasibility of careful study design in molecular studies and its scientific benefits for discovering molecular biomarkers and developing molecular classifiers. Through empirical and simulated studies, we showed that balanced array assignment can effectively improve the accuracy of detecting disease markers. We also showed that balanced array assignment can restore the validity of cross-validation for error estimation in molecular classification. Careful study design based on blocking, stratification, and randomization should be used to more fully reap the benefits of genomics technologies.