Methods, variance, and error in psychoneuroimmunology research: The good, the bad, and the ugly

Every researcher deals with error at some level. In psychoneuroimmunology (PNI) research, there may be error due to substantive fluctuations in immune parameters (e.g., as related to stress, time of day, or activity). This error is significant for some parameters, but it can and should be minimized by taking multiple measurements or converted into "good," substantive variance by measuring variables that can predict the fluctuations. Type I and Type II "bad" errors are of more concern; many PNI studies have far too few subjects for the number of effects they test. Of studies included in a recent meta-analysis of stress and human immunity, several studies actually had fewer subjects than they had statistical tests. Finally, variance due to assay or supply variability contributes to "ugly" error, and it should be addressed by analysis of covariance or partial variance. However, too often, important variance due to factors such as age is designated as "ugly" rather than incorporated into the model. We suggest solutions for addressing "good," "bad," and "ugly" error and look into the future of physiometrics.