Bias in clinical research affects not only the internal validity of studies but also the equitable distribution of health benefits derived from studies. Among the most impactful forms are selection bias, attrition bias, and algorithmic bias, each of which is capable of distorting participant representation, treatment effect estimates, and model performance across important subgroups. This scientific statement provides a reference for cardiovascular researchers and clinicians, integrating detection, correction, and prevention strategies to address these biases. Selection bias may be mitigated through approaches such as inverse probability weighting and adjustment for sociodemographic imbalances; attrition bias can be addressed using intention-to-treat analyses and multiple imputation for missing data that are missing at random; algorithmic bias requires fairness-aware modeling, diverse training data sets, and explainable artificial intelligence techniques. These 3 forms of bias are not exhaustive, but their careful management is essential to achieving scientific rigor, fairness, and real-world applicability, and requires multidisciplinary collaboration to embed equity and validity throughout the research lifecycle.