Reproductive medicine is imbued with debates over the results of key trials. This has resulted in heterogeneity in clinical practice and a disconnect between researchers and the patient group they aim to treat. The criticisms of trials originate from the nature of reproductive health conditions and limitations imposed in designing trials to assess effect in a patient group with heterogenous pathologies leading to the same condition. This leads to challenges in balancing the difficulties of recruiting an enriched patient cohort versus the dilutionary effect and need for subgroup analysis from wider recruitment. These challenges manifest as a failure to achieve traditional statistical significance. One potential solution to overcoming these inherent challenges is that of a Bayesian statistical approach. Using examples from the literature we demonstrate the benefits of a Bayesian approach. Taking published data and using a flat prior (no background information used), a Bayesian re-analysis of the PRISM and EAGeR trials is presented. This demonstrated a 94.7% chance of progesterone and a 95.3% probability of aspirin preventing miscarriage, in contrast to the original trial conclusions. These highlight the role a Bayesian approach can play in overcoming the challenges of trials within reproductive health.