We put forward an adaptive alpha which changes with the amount of sample information. This calibration may be interpreted as a Bayes–non-Bayes compromise, and leads to statistical consistency. The calibration can also be used to produce confidence intervals whose size takes in consideration the amount of observed information.NIH Grant: P20-RR016470. M.E. Pérez’s research was also sponsored by NSF Grant: HRD 0734826.