I have data points which depend on a parameter and for each parameter they are Poisson distributed. Usually I fit them with a Gaussian as a function of that parameter. An important question is whether I should use one or two Gaussians (close together). Apparently two Gaussian close enough together look similar to one and therefore fit well. Even more, if both Gaussians fits converge to the same center point. However my sparse data has large error bars and flattish looking peak rather converge to two separated Gaussians. How could I get a meaningful measure whether my data is one or two Gaussians? I know only basic statistics, but I'm quite OK with general maths.