2026ApJ...999...51B

The minimum variability timescale (MVT) is a key observable for probing the central engines of gamma-ray bursts, constraining both the emission region size and outflow Lorentz factor. However, its interpretation is often ambiguous: Statistical noise and analysis choices can bias measurements, making it difficult to distinguish genuine source variability from artifacts. Here, we perform a comprehensive suite of simulations to establish a quantitative framework for validating MVT measurements based on the Haar wavelet estimator. We show that in multicomponent light curves, the MVT returns the most statistically significant structure in the interval, which is not necessarily the fastest intrinsic timescale, and can therefore converge to intermediate values. Reliability is found to depend jointly on the MVT value and its signal-to-noise ratio (SNR_MVT), with shorter intrinsic timescales requiring proportionally higher SNR_MVT to be resolved. We use this relation to define an empirical MVT validation curve, and provide a practical workflow to classify measurements as robust detections or upper limits. Applying this procedure to a sample of Fermi Gamma-ray Burst Monitor bursts shows that several published MVT values are better interpreted as upper limits. These results provide a path toward standardizing MVT analyses, and highlight the caution required when inferring physical constraints from a single MVT measurement in complex events.