Decision makers need more than just a description of evidence – they need to know what the evidence means for their decisions. That is what BASIE (Bayesian Interpretation of Estimates) was designed to do. Given all the evidence, BASIE produces probability statements — how likely is it that this program truly works? how likely is the true effect to exceed a meaningful threshold? — that map directly onto your decision criteria, not an arbitrary criteria chosen by a researcher (like whether a p-value is less than 0.05). BASIE is described in detail in the resources below and has been applied across evaluations sponsored by federal agencies and foundations.

BASIE Demo is a browser-based demonstration of the BASIE framework applied to a portfolio of estimates. Explore the pre-loaded example to see how Bayesian reinterpretation changes the picture. The demo produces posterior probabilities and credible intervals for each estimate in the portfolio.

The pre-loaded example illustrates a local foundation facing a common challenge: how the standard significance-based approach to picking grant winners is systematically misleading — and how BASIE provides a better path forward.

BASIE has been applied across a wide range of federal evaluation portfolios — spanning health, education, and economic self-sufficiency — demonstrating that its core approach to honest uncertainty is relevant wherever rigorous evidence is used to inform decisions.