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Argus: Interactive A Priori Power Analysis

Xiaoyi Wang 1 Alexander Eiselmayer 1 Wendy E. Mackay 2 Kasper Hornbaek 3 Chat Wacharamanotham 1
2 EX-SITU - Extreme Situated Interaction
Inria Saclay - Ile de France, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, IaH - Interaction avec l'Humain
Abstract : A key challenge HCI researchers face when designing a controlled experiment is choosing the appropriate number of participants, or sample size. A priori 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.
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https://hal.archives-ouvertes.fr/hal-03153651
Contributor : Wendy Mackay <>
Submitted on : Friday, February 26, 2021 - 3:43:02 PM
Last modification on : Saturday, May 1, 2021 - 3:47:12 AM

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Xiaoyi Wang, Alexander Eiselmayer, Wendy E. Mackay, Kasper Hornbaek, Chat Wacharamanotham. Argus: Interactive A Priori Power Analysis. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2021, Transactions on Visualization and Computer Graphics, 27 (2), pp.432-442. ⟨10.1109/TVCG.2020.3028894⟩. ⟨hal-03153651⟩

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