Modern fertility care is defined by treatment options such as In Vitro Fertilization (IVF) that take place over a fixed period (either one or more menstrual cycles) and result in clearly definable success or failure in achieving the desired outcome, a live birth. With over 300,000 cycles involving Assisted Reproductive Technologies (ART) in the United States each year, a significant amount of data can be used to inform optimal practice and assess relevant patient characteristics in counseling. Exciting developments in artificial intelligence (AI) and other advances in interpreting existing data sets offer promise to improve fertility care. As in other areas of medicine, retrospective studies are popular as they are time and cost-effective. However, these studies are subject to bias and lead to less reliable conclusions than prospective analyses. This collection will highlight studies that look at the strengths and limitations of utilizing retrospective cohorts to conclude fertility treatment. Newly published retrospective works can be highly influential in the field. Thus, it becomes paramount to highlight what these studies can and cannot tell us.