Specification setting is a critical component in the development of every new pharmaceutical product. Many specifications are set based on compendial, clinical, safety, and efficacy limits. However, there are times when specifications are set based on non-clinical data collected during development and manufacturing transfer. Statistics plays an important role here, leveraging the knowledge gained through development and in particular based on the analytical data collected from relevant scale batches. Setting data-driven specifications is challenging due to the sample size available at the time the specification is required. This presentation covers an adaptive approach based on Tolerance Intervals, with consideration given to both large and small sample scenarios, as well as producer risk vs. consumer risk. The approach and summary measures will be presented to help understand the risks under different scenarios.