This tutorial provides a comprehensive guide to conducting simulation studies in biostatistics, emphasizing the importance of proper design, execution, analysis, and reporting. Simulation studies are computer experiments that create data through pseudo-random sampling, allowing researchers to understand the behavior of statistical methods by comparing them with known 'truths' (parameters of interest). The tutorial outlines a structured approach called ADEMP (Aims, Data-generating mechanisms, Methods, Estimands, Performance measures) for planning and reporting simulation studies. It also offers guidance on coding, computational considerations, and the estimation of key performance measures, including bias, precision, and coverage. The tutorial emphasizes the importance of reporting uncertainty measures, such as Monte Carlo standard errors, and provides recommendations for presenting results in tables and graphs. Additionally, it reviews 100 articles from *Statistics in Medicine* Volume 34 to identify areas for improvement in simulation study practices. The tutorial aims to enhance the quality and transparency of simulation studies, ensuring they are well-designed and critically appraised.This tutorial provides a comprehensive guide to conducting simulation studies in biostatistics, emphasizing the importance of proper design, execution, analysis, and reporting. Simulation studies are computer experiments that create data through pseudo-random sampling, allowing researchers to understand the behavior of statistical methods by comparing them with known 'truths' (parameters of interest). The tutorial outlines a structured approach called ADEMP (Aims, Data-generating mechanisms, Methods, Estimands, Performance measures) for planning and reporting simulation studies. It also offers guidance on coding, computational considerations, and the estimation of key performance measures, including bias, precision, and coverage. The tutorial emphasizes the importance of reporting uncertainty measures, such as Monte Carlo standard errors, and provides recommendations for presenting results in tables and graphs. Additionally, it reviews 100 articles from *Statistics in Medicine* Volume 34 to identify areas for improvement in simulation study practices. The tutorial aims to enhance the quality and transparency of simulation studies, ensuring they are well-designed and critically appraised.