The short answer
A forecast should describe uncertainty rather than pretend scope and capacity are fixed. Evaluate whether its range is calibrated against actual delivery.
The question and scope
This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate agile sprint forecasting in a documented setting. No measurements, participants or client outcomes are claimed here.
An illustrative test case
Halfway through a sprint an external dependency changes the acceptance scope. Record the change separately so forecast error is not incorrectly attributed only to the team's execution.
This scenario defines a case to examine; it is not an observed result.
A proposed investigation
Compare a fixed estimate, a historical-throughput forecast and a scenario-based forecast on a documented backlog. Record scope changes before checking accuracy. Evaluate ranges as well as point estimates.
Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.
What to measure
- Forecast coverage.
- error.
- scope volatility.
- unused capacity.
Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.
Interpretation and limitations
Historical throughput may stop representing the team after major changes. Report the reference period and capacity assumptions.
Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.
A practical review checklist
- Is the question narrow enough for the available system or dataset?
- Are the comparison conditions genuinely comparable?
- Can a reader trace each conclusion to an observation?
- Are the boundaries and unresolved assumptions visible?
- Does the decision change if the result is negative or inconclusive?
The publication artifact
The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.
Sources and further reading
These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.