The residuals of a correctly parametrized DRT fit should not contain any systematic, non-random features . Furthermore, no assumptions are necessary regarding the number of underlying processes and their distribution. A DRT analysis provides the number of distinguishable processes and their distribution within the sensitivity limit of the data. If the signal-to-noise ratio is not sufficient to distinguish two processes, they are represented as one broadened mode whose width represents the uncertainty of the data. One of the main challenges with this technique is the heteroscedasticity (non-uniformity) of noise in techniques such as EIS, where data is recorded over a frequency range spanning several orders of magnitude . To extract the maximum amount of available information from an EIS measurement by DRT, the frequency dependence of the noise needs to be considered . Past approaches to DRT often used uniform data weighting , which causes local over-or under-regularization, potentially even both in the same data set. Thereby, through over-regularization, information contained in the data is partly suppressed, producing an overly smooth DRT curve and non-random, systematic residuals, while under-regularization produces a DRT curve with physically implausible features such as narrow spikes or oscillations that often represent an overfitting of noise. A more coarse-grained alternative is to conduct a piece-wise DRT calculation, where regions of similar noise values are individually inverted . This approach is, however, rather laborious, and a robust implementation that does not require user intervention is not straightforward.