estimating methods include numeric sizing

Last post 08:32 am December 28, 2013
by Om Prakash Bang
3 replies
11:28 am December 26, 2013

I have read today about scrum effort estimation methods.

"What does the process of estimation look like? Scrum does not prescribe a single way for teams to estimate their work. However, it does ask that teams not estimate in terms of time, but, instead, use a more abstracted metric to quantify effort. Common estimating methods include numeric sizing (1 through 10), t-shirt sizes (XS, S, M, L, XL, XXL, XXXL), the Fibonacci sequence (1, 2, 3, 5, 8, 13, 21, 34, etc.),..."

Question1: I would like to know your experience/ preference about methods for effort estimation metric (M1: 1...10 / M2: T-shirt size / M3: Fibonacci sequence)

Question2: Don't we need for the M2 an extra numeric values mapping list i.e. (XS, S, M, L, XL, XXL, XXXL) = (1, 2, 3, 4, 5, 6, 7) for calculation of sprint velocity?

Question3: Fibonacci sequence sounds to me very sientific and interessting but what is the background/ advantages for its usage? does complexity of user stories follow not liniar pattern?


12:53 pm December 26, 2013

You might like to search the forum for earlier discussions on estimation . Here's a recent thread:

I wrote an article on this subject earlier this year:

08:40 pm December 26, 2013

@Ian Mitchell: your article was very helpful. Thanks again (Mehdi)

08:32 am December 28, 2013

EPIC Estimation : T Shirt Sizing - (XS, S, M, L, XL, XXL, XXXL).
We can do epic estimation with non linear series. When this epic is broken down to user story and estimation, let's not do math, because it's not necessary that sum of user stories points equal to epic should be equal to epic point.

User Story Estimation : Non Linear Series : Fibonacci sequence ( 1, 2, 3, 5, 8, 13, 21, 34, 59, 93 .. ) OR Rounded Fibonacci sequence (1,2,3,5,8,13,20,40, 60,100). While estimating user story we also need to consider three factors - Complexity, Effort and Unknown ( uncertainty ).

Why Non Linear Scale ? We are good in relative sizing, we can do better detailing of the object which is near to us when compared to the object which is very far.

Linear Scale :

2 Series (0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28,30)
4 Series (0,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64,68,72,76,80,84,88,92,96, 100)

Even if you take 4 Series, see how many buckets you have from 20 to 100. If I have to do estimation on linear scale, then I will look for more details of the object even it is big in size.

Non Linear scale estimation is faster.