T-Shirt Sizing: Difference between revisions
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==T-Shirt Sizes== | ==T-Shirt Sizes== | ||
Just like the name suggests, common T-Shirt sizes include: | Just like the name suggests, common T-Shirt sizes include: | ||
* small ('''S''') | * small ('''S''') | ||
* medium ('''M''') | * medium ('''M''') | ||
| Line 14: | Line 13: | ||
* extra large ('''XL''') | * extra large ('''XL''') | ||
* extra extra large ('''XXL''') | * extra extra large ('''XXL''') | ||
==Relative Sizing== | |||
An approach that is common in [[Agile Estimation]] is to use relative sizing to size items relative to each other. One common technique for T-Shirt Sizing is to use a 2 series where an '''S''' is equivalent of one unit of work or effort required to complete the item, and a '''M''' is equivalent to 2 x '''S''' and so on: | |||
* '''XS''' = 0.5 x '''S''' | |||
* '''S''' = 1 | |||
* '''M''' = 2 x '''S''' | |||
* '''L''' = 2 x '''M''' | |||
* '''XL''' = 2 x '''L''' | |||
* '''XXL''' = 2 x '''XL''' | |||
[[File:T-Shirt_Sizing.jpeg|500px|T-Shirt Sizing]] | |||
This series then provides an exponential scale to work with and can be used in conjunction with [[Affinity Estimation]] for example. | |||
==See Also== | ==See Also== | ||
Latest revision as of 01:59, 16 June 2021
T-Shirt sizing is a very popular technique to provide relative abstract sizing for backlog items.
T-Shirt Sizes
Just like the name suggests, common T-Shirt sizes include:
- small (S)
- medium (M)
- large (L)
For additional granularity of sizes the below may also be used:
- extra small (XS)
- extra large (XL)
- extra extra large (XXL)
Relative Sizing
An approach that is common in Agile Estimation is to use relative sizing to size items relative to each other. One common technique for T-Shirt Sizing is to use a 2 series where an S is equivalent of one unit of work or effort required to complete the item, and a M is equivalent to 2 x S and so on:
- XS = 0.5 x S
- S = 1
- M = 2 x S
- L = 2 x M
- XL = 2 x L
- XXL = 2 x XL
This series then provides an exponential scale to work with and can be used in conjunction with Affinity Estimation for example.