% For comparison, we'll generate an "ideal" random dataset 
    % (ideal in the sense that its best-fit 
    % center value exactly matches the model center).

generate_ideal_random_dataset (Lo, Hi);

    % Generate confidence contours for a Gaussian fit
    % to this "ideal" dataset:
    % (for fun, we'll use a different grid)

py = conf_grid ("gauss(1).sigma", 0.015, 0.045, 64);
ic = conf_map_counts (px, py);
   
    %  Now overplot those confidence contours to
    %  compare with the ones from the "random" data
  
oplot_conf (ic);
plot_close (id);

    % To compare with the contour plots, 
    % generate N random datasets, fit a Gaussian to each, 
    % and plot the distribution of best-fit center values:

fit_random_datasets (Lo, Hi, 10000);