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As we prepare to enter the advanced-detector era of ground-based gravitational-wave (GW) astronomy, it is critical that we understand the abilities and limitations of the analyses we are prepared to conduct. Of the many predicted sources of GWs, binary neutron star (BNS) coalescences are paramount; their progenitors have been directly observed \cite{Lorimer_2008}, and the advanced detectors will be sensitive to their GW emission up to $\sim 400~\mathrm{Mpc}$ away \citep{2013arXiv1304.0670L}.  When analyzing a GW signal from a circularized compact binary merger, strong degeneracies exist between parameters describing the binary (e.g., distance and inclination). To properly estimate any particular parameter(s) of interest, the marginal distribution is estimated by integrating the joint posterior probability density function (PDF) over all other parameters. In this work we sample the posterior PDF using software implemented in the \textsc{LALInference} library \cite{Veitch_2014}, \citep{Veitch_2014},  specifically we use results from \textsc{LALInfernce\_nest} \citep{Veitch_2010}, a nest sampling algorithm \citep{Skilling2006}, and \textsc{LALInference\_MCMC} \citep{Christensen_2003,R_ver_2006,van_der_Sluys_2008}, a Markov-chain Monte Carlo algorithm \citep[chapter 12]{Gregory2005}. Previous studies of BNS signals have largely assessed parameter constraints assuming negligible neutron-star (NS) spin, restricting models to nine parameters. This simplification has largely been due to computational constraints, but the slow spin of NSs in short-period BNS systems observed to date \citep[e.g.,][]{Mandel_2010} has also been used for justification. However, proper characterization of compact binary sources \textit{must} account for the possibility of non-negligible spin, otherwise parameter estimates will be biased. This bias can potentially lead to incorrect conclusions about source properties, and even misidentification of source classes \citep{Buonanno_2009,Berry_2014}. 
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