  A determination of the mass function (MF) of stellar clusters can be quite
dependent on the range of measured masses, the fitting technique, and the
analytic function that is being fit to the data. Here, we use HST/WFPC2 data of
NGC 1711, a stellar cluster in the Large Magellanic Cloud, as a test case to
explore a range of possible determinations of the MF from a single dataset. We
employ the analytic modified lognormal power-law (MLP) distribution, a hybrid
function that has a peaked lognormal-like body and a power-law tail at
intermediate and high masses. A fit with the MLP has the advantage that the
resulting best-fit function can be either a hybrid function, a pure lognormal,
or a pure power law, in different limits of the function. The completeness
limit for the observations means that the data contains masses above $\sim
0.90\,M_{\odot}$. In this case, the MLP fits yield essentially a pure power-law
MF. We demonstrate that the nonlinear regression/least-squares approach is not
justified since the underlying assumptions are not satisfied. By using maximum
likelihood estimation, which is independent of binning, we find a best-fit
functional form $dN/d\ln m \propto m^{-\alpha}$, where $\alpha = 1.72 \pm 0.05$
or $1.75 \pm 0.05$ for two different theoretical isochrone models,
respectively. Furthermore, we explore the possibility of systematic errors in
the determination of the power-law index due to the depth of the observations.
When we combine the observational data with artificially generated data from
the lognormal Chabrier IMF for masses below $0.90\, M_{\odot}$, the best fit
MLP is a hybrid function but with a steeper asymptotic slope i.e., $\alpha =
2.04 \pm 0.07$. This illustrates the systematic uncertainties in commonly used
MF parameters that can depend on the range of data that is fitted.
