In the lectures on monte carlo simulation two methods were discussed to estimate the statistical distribution that generates the simulations: \ \ I. choosing a statistical distribution by examining a histogram of the sample data, choosing a statistical distribution with a reasonably similar "probability density function" (example: a roughly bell curve-shaped histogram motivating the choice of a normal distribution), and then using solver to find the parameter(s) of the chosen statistical distribution so that the "cumulative density function" reasonably matched that of the sample data.\ \ II. assuming that the sample data represents the future observations that are possible, and that each the observations in the sample is equally likely to occur in the future.\ \ True or False: If you have a large number of sample observations and are confident that observations in the future will not differ from what has already been observed in the the past, method I is more appropriate than method II.