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    This summary is machine-generated.

    Researchers can now determine optimal sample sizes for experiments using Argus, an interactive tool that visualizes statistical power. This helps in designing better controlled experiments by considering factors like participant fatigue.

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    Area of Science:

    • Human-Computer Interaction (HCI)
    • Experimental Design
    • Statistical Power Analysis

    Background:

    • Determining appropriate sample size is a critical challenge in designing controlled experiments.
    • A priori power analysis is essential for calculating statistical power, considering factors like participant order and fatigue effects.

    Purpose of the Study:

    • To introduce Argus, an interactive tool designed to aid researchers in exploring statistical power.
    • To enable informed decisions regarding sample size by visualizing power across different experimental scenarios.

    Main Methods:

    • Argus simulates data based on researcher-specified experiment designs, including confounds and effect sizes.
    • The tool visualizes statistical power, allowing interactive exploration of trade-offs.

    Main Results:

    • Argus facilitates the interactive weighing of various experimental design trade-offs.
    • Researchers can make more informed decisions about sample size selection.

    Conclusions:

    • Argus provides a valuable method for enhancing the design of controlled experiments.
    • The tool supports researchers in optimizing sample size through interactive power analysis.