I have an aggregated dataframe where each row contains a mean, standard deviation, and sample size N similar to below:
Mean | Stdev | N
$1023 | 1507 | 23
$3951 | 4136 | 17
$864 | 306 | 112
And so on for about 4000 entries. The two histograms below show the distribution of the means, and the distribution of the log-adjusted means, respectively.
I thought about creating N random samples drawn from the Normal(mean, stdev) of each row, but clearly I can't assume they are normal.
I want to fit a probability distribution to the dataframe so I can randomly sample from that estimated distribution. I haven't been able to fit distributions to the regular or log adj distributions because they are bimodal.
How does one typically create the best representative probability distribution of the original dataframe I have?