The heat inactivation of microbial spores and the mortality of vegetative cells exposed to heat or a hostile environment have been traditionally assumed to be governed by first-order reaction kinetics. The concept of thermal death time and the standard methods of calculating the safety of commercial heat preservation processes are also based on this assumption. On closer scrutiny, however, at least some of the semilogarithmic survival curves, which have been considered linear are in fact slightly curved. This curvature can have a significant effect on the thermal death time, which is determined by extrapolation. The latter can be considerably smaller or larger depending on whether the semilogarithmic survival curve has downward or an upward concavity and how the experimenter chooses to calculate decimal reduction time. There are also numerous reports of organisms whose semilogarithmic survival curves are clearly and characteristically nonlinear, and it is unlikely that these observations are all due to a mixed population or experimental artifacts, as the traditional explanation implies. An alternative explanation is that the survival curve is the cumulative form of a temporal distribution of lethal events. According to this concept each individual organism, or spore, dies, or is inactivated, at a specific time. Because there is a spectrum of heat resistances in the population—some organism or spores are destroyed sooner, or later, than others—the shape of the survival curve is determined by its distributions properties. Thus, semilogarithmic survival curves whether linear or with an upward or a downward concavity are only reflections of heat resistance distributions having a different, mode, variance, and skewness, and not of mortality kinetics of different orders. The concept is demonstrated with published data on the lethal effect of heat on pathogens and spores alone and in combination with other factors such as pH or high pressure. Their different survival patterns are all described in terms of different Weibull distributions of resistances as a first approximation, although alternative distribution functions can also be used. Changes in growing or environmental condition shift the resistances distribution's mode and can also affect its spread and skewness. The presented concept does not take into account the specific mechanisms that are the cause of mortality or inactivation—it only describes their manifestation in a given microbial population. However, it is consistent with the notion that the actual destruction of a critical system or target is a probabilistic process that is due, at least in part, to the natural variability that exists in microbial populations.