  The majority of historical designs are a priori in nature, where a priori
indicates a design can be specified in advance of the experiment. The
conventional wisdom is that the set of a priori designs is sufficient to
produce efficient experiments. This work challenges this convention and finds
that efficiency requires data dependent strategies. Specifically, in the
context of a sequential experiment, where observations are accrued in a series
of runs, an adaptive design is proposed that is guaranteed to be more efficient
than any corresponding a priori design.
