Empirical research about tax evasion and the informal economy has exploded in the past few decades, seeking to shed light on the magnitude and (especially policy) determinants of these phenomena. Quantitative information informs the analysis of policy choices, enables the testing of hypotheses about determinants of this phenomenon, and can help with the accurate construction of national income accounts. Even as empirical analysis has burgeoned, some have expressed doubts about the quality and usefulness of some prominent measures. The fact that high-quality data is elusive is neither surprising nor a coincidence. The defining characteristic of tax evasion and informal economic activity—that they are generally illegal—often renders unreliable standard data collection methods such as surveys. Unlike invisible phenomena in the natural sciences, these invisible social science phenomena are hard to measure because of choices made by individuals. Analysis of tax evasion and the informal economy must proceed even in the absence of the direct observability of key variables, and theory should guide the construction and interpretation of evidence of the “invisible.” In this paper, we address what can be learned using micro or macro data regarding tax evasion and the informal economy under given conditions and assumptions, and critically review some of the most common empirical methods in light of our conclusions. We conclude with an entreaty for researchers in this field to enlist in the “credibility revolution” (Angrist and Pischke in J. Econ. Perspect. 4(2):3–30, 2010 ) in applied econometrics.