Abstract Interest has been growing in testing for nonlinearity or chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We designed and ran a single-blind controlled competition among five highly regarded tests for nonlinearity or chaos with ten simulated data series. The data generating mechanisms include linear processes, chaotic recursions, and non-chaotic stochastic processes; and both large and small samples were included in the experiment. The data series were produced in a single blind manner by the competition manager and sent by e-mail, without identifying information, to the experiment participants. Each such participant is an acknowledged expert in one of the tests and has a possible vested interest in producing the best possible results with that one test. The 2000 observation case was large enough to support the use of asymptotic inference, and (3) the inclusion of a noisy chaotic case. But the computational burdens upon the participants in this competition were already pressing the limits that could reasonably be expected of those courageous enough to subject their tests to this professionally risky competition.