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T-shirt sizing

Learn what T-shirt sizing is and how teams use it to compare feature size quickly before detailed estimation.

T-shirt sizing is a lightweight estimation method that groups work into broad categories like XS, S, M, L, and XL.

What is T-shirt sizing?

The method helps teams discuss relative size without pretending they already know exact hours. It is useful in early workshops, roadmap discussions, and scope prioritization sessions.

Why T-shirt sizing matters

  • It is fast and easy to use.
  • It helps organize a long list of features quickly.
  • It supports prioritization before detailed scoping.
  • It works well when precision would be misleading.

Example

A simple profile page may be sized as small, while a custom real-time video workflow may be sized as extra large.

How Apropo supports the workflow around t-shirt sizing

Apropo does not expose a native t-shirt sizing workflow in the confirmed frontend, but it can support the surrounding estimation workflow through structured scope, reusable estimate assets, and versioned review.

  • Hierarchical scope helps teams prepare estimation discussions around concrete modules, features, and tasks.
  • Reusable templates and library elements reduce the effort of setting up the same kind of sizing session repeatedly.
  • Versioned estimate variants make it easier to compare alternative outcomes after a planning discussion.
  • Shareable proposal snapshots help teams review one estimate state with stakeholders after the session.

How Apropo helps refine work after t-shirt sizing

That makes Apropo useful before and after the t-shirt sizing exercise, even if the method itself happens outside the product.

  • Comments and share links help document follow-up clarifications around the chosen estimate shape.
  • Jira export helps move a reviewed estimate structure closer to implementation planning.
  • PDF and proposal exports make it easier to communicate the outcome beyond the delivery team.
  • Budget tracking can later compare planned versus actual work and improve future estimation decisions.

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