Efficiency Requires Adaptation

TL;DR

This paper demonstrates that adaptive, data-dependent experimental designs outperform traditional a priori designs in sequential experiments, leading to more efficient data collection.

Contribution

It introduces an adaptive design approach that guarantees greater efficiency than any fixed a priori design in sequential experiments.

Findings

Adaptive designs outperform a priori designs in efficiency.

Sequential experiments benefit from data-dependent strategies.

The proposed method guarantees improved efficiency.

Abstract

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.