Conducting real-world studies inevitably faces the challenge of bias. Researchers need to employ appropriate bias analysis methods to determine whether the strength of causal associations derived from the study is distorted by potential biases. Traditional qualitative assessments and statistical methods often struggle to effectively quantify the impact of unknown or unmeasured residual biases on study conclusions. Quantitative bias analysis can address this by constructing quantitative models to quantify sources of bias (such as misclassification or unmeasured confounding) and systematically evaluate the direction and magnitude of the effect of residual biases on effect estimates after conventional analysis, thereby confirming the robustness of the estimates. This method has gained widespread international recognition for addressing key questions such as, "How much uncertainty might potential biases introduce?" and "How credible are the study conclusions in the presence of bias?" However, there is still a lack of systematic introduction to this method domestically. Therefore, this article will begin with the practical challenges of bias analysis in real-world studies and sequentially elaborate on the development, theoretical framework, implementation cases, and application of quantitative bias analysis in real-world research. It aims to systematically introduce the methodological characteristics of quantitative bias analysis, providing researchers with a methodological reference for addressing bias analysis issues.