PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 13, 20260 citationsOpen Access

PowerMeter - User Friendly Power Analysis for HCI Studies

View Full Paper
FMFlorin MartiusLSLukas StruckNBNele Borgert

Key Points

Key points are not available for this paper at this time.

Abstract

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Martius et al. (2026) studied this question.

synapsesocial.com/papers/6a10be7cacd1dbe064645559https://doi.org/10.1145/3772363.3799267
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences2007 · 65,955 citations
  2. 2pwr: Basic Functions for Power Analysis2006 · 617 citations
  3. 3Computer Self-Efficacy: Development of a Measure and Initial Test1995 · 6,376 citations
  4. 4Local Standards for Sample Size at CHI2016 · 633 citations
  5. 5Deciding equivalences among conjunctive aggregate queries2007 · 140 citations