In the field of drug development and regulatory science, real-world evidence (RWE) has progressively emerged as a pivotal component. The United States enacted "the 21st Century Cures Act" in December 2016 to encourage Food and Drug Administration (FDA) to leverage real-world evidence, while the European Medicines Agency (EMA) has advanced the development and utilization of RWE through relevant initiatives. In January 2020, China issued the "Guideline on the Use of Real-World Evidence to Support Drug Development and Review," which has significantly catalyzed the advancement of this domain domestically. This paper examines 3 specific cases encompassing retrospective observational studies, prospective randomized controlled trials, and retrospective cohort studies. It systematically analyzes their respective designs and outcomes, delves into the critical statistical elements involved, and provides detailed insights into the associated statistic considerations. Overall, while real-world research is applicable to a wide range of study objectives and designs and plays a vital role in scenarios where randomized controlled trials (RCTs) are challenging to implement, it is also constrained by issues such as variable data quality, potential biases and inadequate standardization. In light of these challenges, the paper underscores the necessity for real-world research to integrate rigorous study design, robust data governance, and advanced statistical methodologies to ensure the scientific reliability and validity of the findings. Particular emphasis is placed on bias control during the design phase, the importance of suitability assessments in data governance, and the centrality of causal inference in statistical analysis. Additionally, it recommends that key statistical issues be carefully considered during protocol development and that alignment be achieved with the Center for Drug Evaluation (CDE) to facilitate the generation of high-quality, evidence-based regulatory decisions.