Abstract: in this article we discuss the complementarity of laboratory and field data. Experiments offer highly controlled environments that allow precise testing and causal inferences. Survey and field data on the other hand provide information on large and representative samples of people interacting in their natural environment. We discuss several concrete examples how to combine lab and field data and how to exploit potential complementarities. One example describes an experiment, which is run with a representative sample to guarantee control and representativeness. The second example is based on the idea to experimentally validate survey instruments to ensure behavioral validity of instruments that can be used in existing panel data sets. The third example describes the possibility to use the lab to identify causal effects, which are tested in large data sets. Topics discussed in this article comprise the relation of cognitive skills (iq) and risk and time preferences, determinants, prevalence and economic consequences of risk attitudes, selection into incentive schemes and the impact of unfair pay on stress.