Durability and reliability are crucially linked in product validation testing. Typically the product’s life requirement is to be able to withstand specified loading for a given duration with desired reliability and confidence levels. Product validation or durability testing is then used to assess actual product life relative to these requirements. The goal of validation test is to demonstrate that the part is indeed capable of withstanding the loading that it will see in service. It is desirable that lab loading is representative of and correlates with service loading. Fatigue analysis techniques and material data like the stress-life (SN) curve can be used to define equivalent damage test specifications and accelerate tests so a long service life can be replicated quickly in the test lab. The challenge with typical validation test specs is that while fatigue methodologies can be used to address damage correlation and equivalence, testing a single part does not provide information about product reliability. Multiple samples need to be tested, potentially for a longer test duration, in order to understand variability and therefore reliability. Life data analysis using Weibull and other life distributions is crucial to addressing this reliability aspect. Durability, life data, and reliability analyses can help engineers answer critical questions like how long to test and how many parts to test in order to meet these life requirements. This paper will demonstrate how reliability metrics can be addressed during durability validation testing in the lab.