# Parameters of a negative binomial don't match the observed moments

I have a dataset which, by the looks of it, closely resembles a negative binomial distribution. It is the number of minutes an event has lasted. Here is the original data:

I calculate the mean and variance of my data. Mean = 19.08, Variance = 54.67. I try to find out the parameters with which a negative binomial distribution would have the same mean and variance to see if it looks anything like my sample. I see in the literature people work with different parameters - normally they choose two of the p, r, the mean and the variance. Since I have made an assumption for the mean and the variance I decide to look for p and r. Using the following formulas for the mean and the variance (courtesy of Wikipedia):

... I arrive at p = 0.65 & r = 10.23. If I understand correctly r that is not an integer is perhaps not very logical but computationally possible. Then I calculate the pmf for the single values of X and compare with my sample. To my surprise the negative binomial distribution I have simulated looks nothing like my sample:

I decide to start playing around with the parameters and with certain values I get pretty close to what I observe. For example, for p = 0.375 and r = 11 it looks like this:

Now that looks pretty much like the distribution of my sample. However, using the Wiki formulas from above, those p and r values lead to a mean = 6.6! It is obvious from the diagram alone that that doesn't make sense. The graph peaks around 17, and is a bit skewed to the left, so the mean should be somewhat bigger than that. Now I am confused.

So my question is, why do the p and r values that match my observed mean and variance lead to an entirely different distribution? Or the other way around, why do the p and r values that produce a distribution matching the observed one, lead to a mean that is obviously very wrong? What am I doing wrong here?

Note: All the calculation are done in Excel. Using the Excel formula for a pmf of a negative binomial and calculating it "manually" leads to the same result. As you see it is a very layman's task and I don't aim at the most acurate estimation of the parameters but want to understand where is the mistake in my reasoning and why am I so far off.

Thanks in advance to anyone willing to help!

I ran into a similar problem a couple weeks ago. On Wikipedia, the definition has $$r$$ as the number of failures, and the random variable is the number of successes. But everywhere else, $$r$$ is the number of successes, and the random variable is the number of failures.
Defined in the usual way, the mean of a negative binomial RV is $$\mu = \frac{r(1 - p)}{p}$$ and the variance is $$\sigma^2 = \frac{r(1 - p)}{p^2}$$.
Using these with the $$r$$ and $$p$$ you found, you get $$\mu = 18.33$$ and $$\sigma^2 = 48.88$$, which looks to be more in line with what you're seeing.