The properties of neurotransmitter receptor channels are important for determining synaptic transmission in the nervous system. The presence of quantal variability complicates the use of conventional non-stationary noise analysis for determining the unitary conductance and number of channels involved in synaptic currents. Peak-scaled non-stationary noise analysis has been used to compensate for quantal variability, but there is evidence that the resulting variance versus mean relationships can be transformed from parabolic to skewed. We have used computer modelling based on experimentally derived kinetic schemes to investigate such relationships and demonstrate that their shape is a consequence of the temporal structure of the fluctuations during synaptic responses. Covariance analysis showed that peak-scaling generates a skewed relationship when the covariance function decays rapidly (compared to the average response waveform), corresponding to a low correlation between fluctuations at the peak and in neighbouring regions of the decay phase. A parabolic relationship is obtained when the covariance function decays more slowly, corresponding to a higher correlation. Irrespective of a skewed or parabolic relationship, we demonstrate that the unitary current can be reliably estimated, with a coefficient of variation (CV) as low as 0.05 and bias as low as ±2% under ideal conditions. While the shape of the variance versus mean curve after peak-scaled non-stationary noise analysis is ultimately a consequence of the kinetic properties of the channels, inadequate alignment of individual waveforms can transform the relationship from parabolic to skewed, and low-pass filtering can transform the relationship from skewed to parabolic. These findings have important implications for analysis of experimental data.