While sampling at a Nyquist frequency equal to the highest frequency present in the data (critical sampling) is sufficient to prevent aliasing in both the data and the autocorrelation of a bandlimited energy signal, the sampling requirements for the avoidance of aliasing in higher-order correlations and spectra are not the same. Also, there is a difference in aliasing effects depending on whether one samples the original continuous-time signal and calculates the autocorrelation or one samples the continuous-time autocorrelation. This distinction between sampling procedures must be made for correlations of higher order, as well, for which not only the type of aliasing but also the sampling requirements to prevent aliasing differ. In particular, if one samples the continuous-time autobicorrelation or autotricorrelation, critical sampling is sufficient to prevent aliasing. In practice, however, it is not usually the continuous-time autobicorrelation or autotricorrelation that is sampled. Generally, it is the original continuous-time signal that is sampled and used to calculate the discrete-time autobicorrelation or autotricorrelation. In this case, to prevent aliasing, the sampling interval for the autobicorrelation must be no greater than two-thirds the interval associated with critical sampling, and no greater than one-half for the autotricorrelation. Numerical calculations of autocorrelation, autobicorrelation, and autotricorrelation zero-lag values corresponding to spectral area and volume as well as bispectral contour plots for two model bandlimited energy signals are presented as demonstrations of these conclusions. Sampling requirements for higher-order correlations of rectified signals are also discussed. If the signal is to be rectified for use in a correlation detector or time delay estimator, then the sampling rate must increase to accommodate the higher frequencies that usually result from the process of rectification. Spectral masking filters, which remove aliasing in higher-order correlations, are defined and a bispectral filter is applied to some representative energy signals. Lag domain convolution filters for the removal of aliasing in the bicorrelation and tricorrelation are also given. These filters assume critical sampling or finer for the original signal to remove aliasing effects totally.