A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A prior power analysis examines the relationships among multiple parameters, including the complexity associated with human participants, e.g., order and fatigue effects, to calculate the statistical power of a given experiment design. We created Argus, a tool that supports interactive exploration of statistical power: Researchers specify experiment design scenarios with varying confounds and effect sizes. Argus then simulates data and visualizes statistical power across these scenarios, which lets researchers interactively weigh various trade-offs and make informed decisions about sample size. We describe the design and implementation of Argus, a usage scenario designing a visualization experiment, and a think-aloud study.

中文翻译：

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Argus：交互式先验功效分析

HCI 研究人员在设计受控实验时面临的一个关键挑战是选择合适的参与者数量或样本量。先验功效分析检查多个参数之间的关系，包括与人类参与者相关的复杂性，例如顺序和疲劳效应，以计算给定实验设计的统计功效。我们创建了 Argus，这是一种支持交互式探索统计功效的工具：研究人员指定具有不同混淆和效应大小的实验设计场景。然后，Argus 模拟数据并可视化这些场景中的统计功效，这让研究人员可以交互地权衡各种权衡，并就样本量做出明智的决定。我们描述了 Argus 的设计和实现，这是一个设计可视化实验的使用场景，