  With the growth in experimental studies in education, policymakers and
practitioners are interested in understanding not only what works, but for whom
an intervention works. This interest in the generalizability of a study's
findings has benefited from advances in statistical methods that aim to improve
generalizations, particularly when the original study sample is not randomly
selected. A challenge, however, is that generalizations are frequently based on
small study samples. Limited data affects both the precision and bias of
treatment impact estimates, calling into question the validity of
generalizations. This study explores the extent to which redefining the
inference population is a useful tool to improve generalizations from small
studies. We discuss two main frameworks for redefining populations and apply
the methods to an empirical example based on a completed cluster randomized
trial in education. We discuss the implications of various methods to redefine
the population and conclude with guidance and some recommendations for
practitioners interested in using redefinition.

Methodology;Applications