Auditory spectra are a core element of speech, hearing, and language research, underpinning representations of hearing ability, frequency characteristics in stimuli, and microphone responses, among other data points. However, comparing these spectra presents unique statistical challenges due to the distinct properties of the human auditory system and of acoustic spectra. In particular, non-linear frequency resolution and unequal bandwidths across center frequencies complicate straightforward bin-by-bin measurements. Further, non-linear loudness and power-law relationships (e.g., Stevens’ power law, Fletcher–Munson curves) mean that spectra appearing numerically similar can still sound perceptually different. Correlations among neighboring frequency bins, often introduced by harmonic signals and formant structures, add another layer of complexity, yielding high-dimensional, correlated distributions that cannot be treated as independent and identically distributed. This study explores the statistical characteristics of auditory spectra with two main objectives: (1) evaluating differences in hearing ability among individuals, and (2) comparing distinct auditory spectra. We review standard statistical methods commonly used in hearing and speech sciences and propose enhancements that streamline spectral comparisons, ultimately increasing validity and enabling more detailed interpretation of auditory data.