7 Unsampled or extinct 'ghost' populations leave signatures on the genomes of individuals from extant, 8 sampled populations, especially if they have exchanged genes with them over evolutionary time. This gene 9 flow from 'ghost' populations can introduce biases when estimating evolutionary history from genomic 10 data, often leading to data misinterpretation and ambiguous results. To assess the extent of this bias, we 11 perform an extensive simulation study under varying degrees of gene flow to and from 'ghost' populations 12 under the Isolation with Migration (IM) model. Estimates of popular summary statistics like Watterson's 13 θ, π, and F ST , and evolutionary demographic history (estimated as effective population sizes, divergence 14 times, and migration rates) using the IMa2p software clearly indicate that we a) under-estimate divergence 15 times between sampled populations, (b) over-estimate effective population sizes of sampled populations, 16 and (c) under-estimate migration rates between sampled populations, with increased gene flow from the 17 unsampled 'ghost' population. Similarly, summary statistics like F ST and π are also affected depending 18 on the amount of gene flow from the unsampled 'ghost'. 19 * lynch026@cougars.csusm.edu † asethuraman@csusm.edu 20Studies that apply population genetics methods to infer evolutionary history, including those in the fields 21 of conservation, agriculture, evolution, and anthropology often begin with a sampling strategy. As a 22 rule of thumb, we would expect that the more extensive the sampling of individuals across their geo-23 graphical range (or according to the biological question at hand), the better the resolution of population 24 genetic analyses. However, in any such study, genomic data collected harbors signatures of evolutionary 25 processes of drift, selection, and gene flow involving both sampled and unsampled 'ghost' populations 26