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Abstract:
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Many statistical ideas can be conceptualized as constrained optimization problems with dual objectives. For example, sampling design seeks the greatest precision for a given cost, hypothesis testing trades off Type I and Type II errors, and analyses of incomplete data aim to incorporate all available information while accurately representing uncertainty. In an analogous way, ethics can be conceptualized as a system of structured logic for resolving dilemmas. Reflections by leading statisticians about the nature of our work, scientific insights regarding the imperatives of interpersonal communication, game-theory perspectives on competition and cooperation, and other philosophical discourse on the ethics of human interactions all contribute to reasoning that trust and understanding, viewed as companion principles, offer a foundation for judging whether a statistical approach is ethical. The presentation will include illustrative examples. It is argued that the simplicity of the framework makes it easy to communicate, its generality gives it power, and its positive-sum appeal could be used to promote professional identity development around ethics.
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