Separating the populations in a bimodal distribution I have a data set which displays a bimodal distribution. This was determined by plotting a histogram of the frequency vs number. 
I now need to separate the two original populations and therefore find an intersection point of sorts. From the plot it looks like the point might be approx. -1.0 to -0.8.
Is there a straight forward calculation or function that I can use to locate this point more accurately?

 A: Actually that algorithm sounds like it is using precisely the methodology that Macro was suggesting.  The idea is that you have a distribution $$F(x)=pF_{1}(x)+(1-p)F_{2}(x)$$  where $F_1$ and $F_2$ are specified up to a few parameters that are estimated from the data.  In your case $F_1$ and $F_2$ are both Gaussian and there would be 5 parameters to estimate from the data, $p, σ_1, σ_2, μ_1$, and $μ_2$ the mixture proportion and the standard deviations and means respectively for the distributions $F_1$ and $F_2$.  There is actually no unique separation point since the distributions overlap.  But the algorithm probably picks the crossing point for the densities.  Since these are Gaussian distributions there will only be one unless one has a very large variance compared to the other.  Without knowing the algorithm, I wouldn't know how many of these parameters are estimated.  For example both standard deviations could be estimated or they could be assumed equal and only a pooled standard deviation would be estimated. 5 parameters estimated in the first case and 4 in the second.  After the parameters of $F_1$ and $F_2$ are estimated you use the distributions specified by the estimates to calculate the crossing point.
A: For anyone else interested, I used Gaussian Mixture Modeling (GMM) algorithm to determine the means of the two populations and separate them.
Details of the techniques used are explained in the paper linked on this page:
http://www-personal.umich.edu/~ognedin/gmm/gmm_user_guide.pdf
Gnedin, O. (2010). Quantifying bimodality
A: You can use the Otsu's method, you can think at your histogram as the histogram of the grey values of pixels in an image. 
Then, in computer vision and image processing, Otsu's method, named after Nobuyuki Otsu (大津展之 Ōtsu Nobuyuki)1, is used to automatically perform clustering-based image thresholding, or, and that is your case, the reduction of a graylevel image to a binary image.
The algorithm assumes that the image contains two classes of pixels following bi-modal histogram (foreground pixels and background pixels), it then calculates the optimum threshold separating the two classes so that their combined spread (intra-class variance) is minimal, or equivalently (because the sum of pairwise squared distances is constant), so that their inter-class variance is maximal.

1Otsu, Nobuyuki. "A threshold selection method from gray-level histograms." IEEE transactions on systems, man, and cybernetics 9.1 (1979): 62-66.
A: You can use the check to "normality" (there are many packages):  The algorithm is simple: split var into bins. count the number of observations and the expected, take the difference, standardize by expected. The bin in which we have the min of standardized res - will contain the desired optimal threshold for dichotomisation! (2 bins with max stdRes= modes of yours 2 distributions ;-). Don't fogot  play with (try) differrent bin numbers! Next you try to clarify within this interval via model selection (KLIC/CAIC/BIC/etc).
