Recently i was present with a task estimation technique. Instead of letting people rate a task for x-amount of hours, let them discretize a tasks into discrete sizes like small/medium/large/x-large. (Planning poker from scrum for people aware of this)

After a bit of tracking we should be able to estimate the duration based upon historical data (i.e. statistics) I have been given a set of sample data (real data, but not from my work) and would like see there actually can be made any distribution which fit this. (of course i would need to recalculate this for my on tasks)

The distributions from the presentations look like this:

It seem like some right skewed distribution. From Uni, i recall working with a distibution looking like this, but i am not certain about the name. I was hoping for something parametric where i can derive some simple parameters form the data (like mean and variance for a normal distribution)

I have made a histogram of the data:  

(i know there is too little data for the small sizes)

Hope somebody have suggestions

Recently I was present with a task estimation technique. Instead of letting people rate a task for x  - amount of hours, I let them discretize tasks into discrete sizes like small  / medium / large / x-large. (The tasks are planning poker from scrum for people aware of this.)

After a bit of tracking we should be able to estimate the duration based upon historical data (i.e. statistics). I have been given a set of sample data (real data, but not from my work) and would like see there actually can be made any distribution which fit this. (Of course, I would need to recalculate this for my own tasks.)

The distributions from the presentations look like this: 

It seem like some right skewed distribution. From University, I recall working with a distribution looking like this, but I am not certain about the name. I was hoping for something parametric where I can derive some simple parameters form the data (like mean and variance for a normal distribution)

I have made a histogram of the data: 

(I know there are too few data.)