Hi, I know this is a bit off-topic, but I am quite puzzled. I am going through several papers about aerosol physics and in this field you often have determine the parameters of a distribution to match your experimental data (one typically uses a Gaussian mixture). However, in many cases people plot a normalized empirical distribution function and then perform some least-square fitting rather than using likelihood functions. As an undergrad, I was told that the former approach is correct only if you have a model for the dynamics (e.g. Ohm law and you perform a least-square fitting), but not if you deal with a distribution and you pick random draws from it (in that case, one should maximize the probability of drawing the data which were actually observed and this leads to likelihood functions). The two approaches do not seem equivalent to me, but I cannot believe that this distinction is ignored in practice... Many thanks Lorenzo