Sample sizes in experimental games

Limited use has been made of power analyses in experimental economics. Very often, the outcome of the control group is associated with a random variable of interest that shows little variance, in which case, there is often not much to learn from the control group. In such cases, control groups of lesser size are more desirable for they give the same message with fewer resources. I demonstrate that the central limit theorem cannot be blindly relied upon in experimental economics. I propose a general solution for a class of problems that interest experimental economists both in the field and the lab. I show that even when the distribution of the outcome variable is not known or assumed, one can (non-parametrically) arrive at a satisficing sample size that has sufficient power for testing the null hypothesis of an assumed mean for the control group.