Estimating thresholds to distinguish between good and degraded ecosystem states is key for assessing and managing marine environments. Numerous methods are used to estimate thresholds, however there is no standardised framework to evaluate their accuracy and reliability which reduces the consistency and transparency of thresholds estimated for ‘good’ status. Statistical robustness of four methods was evaluated by varying stochastic noise, sample size, and shape of the pressure-state relationship, of simulated indicator data. Range of natural variation and statistically detectable change methods, which quantify natural variation in undisturbed reference conditions, reliably estimated status thresholds for noisy, small datasets, but thresholds were lower than what would have been estimated without noise present or with a greater sample size. Tipping points and distance to degradation methods, which estimate the point at which a system is about to reach, or has reached, a degraded state, failed to estimate thresholds or fit models that were consistent with the underlying relationship as noise increased and sample size decreased. Therefore, for small or noisy datasets, range of natural variation is most suitable to estimate ecologically meaningful, reliable, and transparent thresholds for good status, while for larger datasets with low noise levels all four methods are likely to be useful.