Determine whether apparent performance parity reflects true equivalence or inadequate statistical power

Determine whether the large p-value comparing the proposed Level 1 smart-navigation mode with the conventional navigation solution reflects true performance parity or insufficient statistical power caused by human variability and the study’s small sample size.

Background

The study compares a conventional edge-detection prosthetic-vision solution with two versions of the proposed smart-navigation system, differing in the maximum number of displayed obstacles. Although the proposed Level 1 mode produced results numerically similar to those of the conventional solution, the comparison involved a small sample and substantial individual variability.

The authors note that the high p-value in the comparison could have two competing interpretations: the modes may genuinely have comparable performance, or the experiment may have lacked sufficient statistical power to detect a real difference. Resolving this issue requires a better-powered evaluation capable of distinguishing genuine performance equivalence from a false negative statistical result.

References

The high p-values (e.g., $p = 0.95$ for K1 versus the conventional solution) leave a critical statistical question open: we cannot be certain if the p-value is large because the two modes share true performance parity, or simply because the study lacked the statistical power to detect the true effect through the noise of human variability.

— Smart Navigation for Visual Prostheses in Virtual Reality: An End-to-End Framework for Priority-Based Scene Translation and Path Guidance  (2610.05772 - Abdellatif et al., 5 Oct 2026) in Section 5.3, “Temporal efficiency” (Discussion)