where τ * -1 is the "characteristic relaxation rate", M 0 and M eq are the initial and equilibrium magnetisation, respectively, and the stretch parameter 0 < β ≤ 1 describes the multiexponential character, with β = 1 corresponding to mono-exponential decay. Historically in the SMM community, the values of τ -1 Debye and τ * -1 have been taken as the relaxation rates of the SMM and α and β were simply ignored. However, these parameters encode vital information about the distribution of relaxation rates in the experimental data (Fig. ). For instance, β in Eq. 2 is known to represent distributions in samples when the SEF is used for the analysis of supercapacitor discharge, x-ray photon correlation spectroscopy, and can indicate the onset of long-range-order in µ-SR measurements. The stretched exponential function is also used extensively in the study of spin-glass phenomena and NMR measurements. Elsewhere, a lack of understanding of the physical relevance of the β parameter has stifled advancement in the lifetime of batteries. Recently, using the known analytical distribution function implied by the Generalised Debye model, we developed a method for approximating estimated standard deviations (ESDs) of magnetic relaxation rates from the α parameter. Accounting for experimental ESDs gives an indication of how strongly trends in the data can be relied upon to avoid over-interpretation, and indeed important information on SMM magnetisation dynamics can be gleaned from the behaviour of the distribution width as a function of temperature and/or magnetic field. In a similar vein, we previously approximated ESDs for rates derived from the SEF, using the β parameter and the SEF distribution function. In that work we followed literature precedent, and our own bias, and ascribed particular importance to the characteristic relaxation rate τ * -1 , using it as the central value around which to define symmetric ESDs on a logarithmic scale. However, unlike the Generalised Debye model, the distribution of the SEF is asymmetric (Fig. ); hence, the "centre" of the distribution changes with its width. Thus, as well as τ * , one may consider the peak of the distribution (the mode), τ mode , and two calculated expectation values of τ, evaluated on a linear (⟨τ⟩) or logarithmic scale (e ⟨ln [τ]⟩ ) as being indicative of "the relaxation rate" (Fig. ). These measures of τ diverge when β < 1 (Fig. ), and given that low values of β (0.4-0.7) are common for SMMs, particularly those with extremely slow relaxation rates, there is a drive to assess which of these values most reliably correspond to "the experimental relaxation rate".