PowerMeter - User Friendly Power Analysis for HCI Studies
Florin Martius, Lukas Struck, Nele Borgert, Anna-Marie Ortloff, Simon Lenau, Kay Schröder, Theo Sans-Raimbault, Leonard Heinrich, Matthew Smith, Christian Tiefenau · 2026
Determining the right sample size for empirical studies is a persistent challenge in human–computer interaction research. Studies with too few participants risk low statistical power and unreliable findings, yet existing tools for power analysis, such as G*Power, are often difficult to use. We present PowerMeter, a user-centered tool that helps researchers estimate appropriate sample sizes for quantitative studies. PowerMeter focuses on usability and interpretability, guiding users through the process of defining key study parameters and understanding the implications of statistical power. In a pilot study (N = 60), we find that participants using PowerMeter produced more accurate sample size estimates and reported higher levels of trust and satisfaction than those using G*Power.