Bayesian methods are emerging as the primary alternative to the conventional frequentist approach to statistical inference. Bayes' theorem is a model for learning from data. Using Bayes’ theorem, a researcher weights their prior beliefs about the size of an intervention’s effect by the data observed through experimentation.
Bayesian analysis results in a point estimate of the intervention’s effect and an interval for the credible value of the effect. With these in hand, the research can answer such questions as:
- What is the probability that the effect of the intervention is greater than zero?
- What is the probability that the effect of the intervention is between values X and Y? (Where X and Y might be values of policy relevance.)
The frequentist perspective cannot answer these types of questions.
OPRE’s 2017 Methods Meeting, Bayesian Methods for Social Policy Research and Evaluation Visit disclaimer page , informed many of our resources on Bayesian inference. This meeting was one in a series of annual meetings in our Methods Inquiries project that bring together expertise from varying disciplines across academia, government, and the private sector. Participants explore innovations in research design, analytic techniques, and data measurement with the potential to advance OPRE‐supported research so that it represents the most scientifically advanced approaches to determining effectiveness and efficiency of ACF programs.
Point(s) of contact: Kriti Jain