Touchstone2 offers a direct-manipulation interface for generating and examining trade-offs in experiment designs. Based on interviews with experienced researchers, we developed an interactive environment for manipulating experiment design parameters, revealing patterns in trial tables, and estimating and comparing statistical power. We also developed TSL, a declarative language that precisely represents experiment designs. In two studies, experienced HCI researchers successfully used Touchstone2 to evaluate design trade-offs and calculate how many participants are required for particular effect sizes. We discuss Touchstone2’s benefits and limitations, as well as directions for future research.

Touchstone2 offers a direct-manipulation interface for generating and examining trade-offs in experiment designs. Based on interviews with experienced researchers, we developed an interactive environment for manipulating experiment design parameters, revealing patterns in trial tables, and estimating and comparing statistical power. We also developed TSL, a declarative language that precisely represents experiment designs. In two studies, experienced HCI researchers successfully used Touchstone2 to evaluate design trade-offs and calculate how many participants are required for particular effect sizes. We discuss Touchstone2's benefits and limitations, as well as directions for future research.

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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