  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.
