  Experimental datasets are growing rapidly in size, scope, and detail, but the
value of these datasets is limited by unwanted measurement noise. It is
therefore tempting to apply analysis techniques that attempt to reduce noise
and enhance signals of interest. In this paper, we draw attention to the
possibility that denoising methods may introduce bias and lead to incorrect
scientific inferences. To present our case, we first review the basic
statistical concepts of bias and variance. Denoising techniques typically
reduce variance observed across repeated measurements, but this can come at the
expense of introducing bias to the average expected outcome. We then conduct
three simple simulations that provide concrete examples of how bias may
manifest in everyday situations. These simulations reveal several findings that
may be surprising and counterintuitive: (i) different methods can be equally
effective at reducing variance but some incur bias while others do not, (ii)
identifying methods that better recover ground truth does not guarantee the
absence of bias, (iii) bias can arise even if one has specific knowledge of
properties of the signal of interest. We suggest that researchers should
consider and possibly quantify bias before deploying denoising methods on
important research data.
