Visualization experiments need a set of "good" stimuli that effectively address research questions and hypotheses. Creating, managing, and deploying stimuli are often challenging, as these tasks require tremendous care. Inappropriate stimuli can make the outcome invalid or uninteresting, wasting both researchers' and participants' resources. As the speed of science increases, better support for stimuli-related tasks is essential, yet we lack a closer look at how visualization researchers deal with them. To understand the experiences of visualization experimenters and guide future improvements, we interviewed 19 visualization researchers with diverse backgrounds and experiences. Our findings describe practices and challenges across the life cycle of stimuli, from exploration and selection through shipment, deployment, and analysis. For example, stimuli management and deployment require tedious manual effort, which does not scale for experiments with many levels and complex conditioning. We also discuss both concerns and optimism around AI-assisted visualization experiment design. We conclude with future research opportunities in supporting stimuli creation, automated stimuli inspection, and experimental apparatus concerns.

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