  The amount of data generated and gathered in scientific simulations and data
collection applications is continuously growing, putting mounting pressure on
storage and bandwidth concerns. A means of reducing such issues is data
compression; however, lossless data compression is typically ineffective when
applied to floating-point data. Thus, users tend to apply a lossy data
compressor, which allows for small deviations from the original data. It is
essential to understand how the error from lossy compression impacts the
accuracy of the data analytics. Thus, we must analyze not only the compression
properties but the error as well. In this paper, we provide a statistical
analysis of the error caused by ZFP compression, a state-of-the-art, lossy
compression algorithm explicitly designed for floating-point data. We show that
the error is indeed biased and propose simple modifications to the algorithm to
neutralize the bias and further reduce the resulting error.
