A logic system for the choice of risk levels in hypothesis testing

We have examined current, traditional practice of establishing risk levels, favoring use of small α and implicitly accepting correspondingly larger β errors. There are both loses and gains associated with this approach. In particular, small a minimizes the probability of erroneously rejecting H_o, but it also increases the chances that a new model, representing a real improvement, may be discarded. There is no overwhelming logic that one type of error is always more important than the other, since relative costs vary with the conditions surrounding the research investigation.