Grain sizes are one example of a very non-normal distribution, and we would not expect that standard deviation (for instance) would have much relevance for these distributions. There are two sets of grain diameters with the same (to two decimal places) $r/r_0$ value, $\approx$0.82. These are both plotted as histograms
Grain sizes are one example of a very non-normal distribution, and we would not expect that standard deviation (for instance) would have much relevance for these distributions. There are two sets of grain diameters with the same (to two decimal places) $r/r_0$ value, $\approx$0.82. These are both plotted as histograms
An important note: Matplotlib's `pyplot.hist` command does not give a sum of the histogram to 1.0, but rather an integral of the histogram to 1.0 (when `density=True`). This is why the peak of the plot above is >1.0.
An important note: Matplotlib's `pyplot.hist` command does not give a sum of the histogram to 1.0, but rather an integral of the histogram to 1.0 (when `density=True`). This is why the peak of the plot above is >1.0.