This chapter begins by describing the consequences of uncertainty about the univariate characteristics of the data for multiple regression analysis. Next, we describe the bounds approach to inference, a general inferential framework for dealing with uncertainty when the exact limiting distribution associated with a test statistic is unknown or difficult to determine. We then introduce a bounds approach to inference that is appropriate when there is uncertainty about the properties of both the outcome and (at least some) explanatory variables. This approach uses critical value bounds to evaluate the null hypothesis of no long-run relationship based on the long-run multiplier (LRM) for each independent variable in either the ADL, GECM, or restricted versions of these models. We also describe the ARDL bounds approach to inference developed by Pesaran, Shin, and Smith, which assumes that the outcome is known to contain a unit root but allows uncertainty about the properties of the explanatory variables. We compare the relative advantages of these frameworks and provide a practical guide to critical bounds approaches to inference with two illustrative examples.