How to determine the type of probability distribution for a dataset? I have aggregated(total) youtube videos views. I have take log of that views. And calculated autoregressive koefs that can be used for the video views predictibility tests. Let say I have aggregated daily views array for each video.  Koef for each video is calculated as:
koef =  aggregatedViews[60]  / aggregatedViews[30]
This list of koefs for all videos forms target distribution.
Initially, I thought that it will be half-normal but looks like this is not normal. Is this Pareto Type I distribution?
Here is 70 bins histogram:
 A: 
How to determine the type of probability distribution for a dataset?

You can use the fitdistrplus package in R. First, you can plot a Cullen AC and Frey graph using the descdist function in order to find possible candidates of distributions . Then you can fit the best candidates of distributions to your data using fitdist. Now you can test the hypothesis that your data comes from these distributions by performing a Kolmogorov-Smirnov test or an Anderson-Darling test. Finally, you can select a fitted distribution using graphical methods or comparing measures of quality like AIC values.
Here you can find a nice example of this procedure:
https://stats.stackexchange.com/a/132700/154523
A: This requires intuition and experimentation.  You should appeal to a QQ plot in order to visually measure your distribution against another.  
Here is a sample distribution that looks like it might be normal.  Let's put it on a QQ plot against a true normal distribution.

The red line represents the normal distribution, and we can see that on the tails, the sample distribution does not conform, therefore we conclude that it is not normal.

Additionally, if you dont trust your eyes, you can use Scikit-Learn's normaltest() to quantify the deviation from the normal distribution.
Note: you can scale your test distribution with StatsModels' qqplot()  when presented with the following QQ plot by playing with the scale parameter

The above QQ plot looks like the following after being scaled
qqplot(standardized_res, dist=normal, line='s', scale=0.7)

