Policy researchers have become increasingly familiar with a number of improved techniques for analyzing data obtained from interrupted time‐series designs for evaluating public programs and policies. In this paper we contribute to this trend by presenting two groups of data analysis techniques which are not currently widely used by policy researchers, but are likely to be valuable adjuncts to traditional regression techniques for analyzing data obtained from interrupted time‐series designs. First, aids for model specification are presented that enable the analyst to define an appropriate linear trend model—often one which will reduce the degree of multicollinearity and, therefore, produce more precise estimates of the impacts of a public program or policy. Next we consider approaches for point estimation and joint (simultaneous) interval estimation of a policy intervention's total effect at various points in