What Is Estimation Best Practices?

Estimation best practices are the principles, techniques, and habits that software teams use to produce accurate, consistent, and useful predictions of how long work will take and how much effort it will require. Good estimation is not about achieving perfect precision but about reducing uncertainty enough to make sound planning decisions. It combines historical data, team calibration, and structured techniques to turn guesswork into informed forecasts.

Software estimation is notoriously difficult. The abstract nature of programming, the variability of codebases, and the frequency of unexpected complexity all conspire against accurate prediction. Yet estimation remains essential because every business decision, from release dates to hiring plans to revenue forecasts, depends on some prediction of when work will be done.

A frequently cited study by the Standish Group found that software projects exceed their original estimates by an average of 66%. While the methodology of that specific study has been debated, the underlying reality is well established: most teams underestimate work more often than they overestimate it. The cost of chronic underestimation includes missed deadlines, rushed releases, increased technical debt, and eroded trust between engineering and business teams.

Estimation best practices do not eliminate uncertainty. They manage it. By using structured techniques, calibrating against historical performance, and communicating estimates as ranges rather than single numbers, teams set expectations that are both useful and honest. For a deeper look at the tools that support this process, see the effort estimation software guide.

Effective estimation begins before any numbers are discussed. The team must have a shared understanding of what "done" means for a given piece of work, including testing, documentation, code review, and deployment. Ambiguity in the definition of done is the single largest source of estimation error.

Once scope is clear, teams apply one or more estimation techniques. Story points use a relative scale to compare the effort of new work against previously completed work. T-shirt sizing (S, M, L, XL) provides a coarser but faster categorization useful in early-stage planning. Planning poker is a consensus-building exercise where each team member independently estimates and the group discusses discrepancies. Time-based estimation in hours or days is sometimes used for well-understood, repeatable tasks.

The most important practice is calibration: regularly comparing estimates to actuals and adjusting the process based on what the data reveals. A team that consistently underestimates large items by 40% can apply a correction factor or break large items into smaller, more estimable pieces. Without this feedback loop, estimation remains a one-time guess rather than an improving skill. For more on connecting velocity data to estimation, see velocity-based estimation.

Project management platforms like Jira, Linear, and Shortcut support estimation workflows with fields for story points, time estimates, and sprint velocity tracking. Specialized tools focus on improving estimate accuracy. Monte Carlo simulation tools like Actionable Agile generate probabilistic forecasts based on historical throughput data, converting "when will this be done?" into a probability distribution rather than a single date.

Glue contributes to better estimation by providing codebase intelligence that reveals the true complexity of proposed work. When a team can see the dependency graph, the test coverage, and the recent change frequency of the code area they are about to modify, their estimates are grounded in data rather than assumptions. This is especially valuable for the software estimation accuracy challenge, where the gap between perceived and actual complexity is the root cause of most estimation failures.

Should teams estimate in story points or hours?

Neither is universally better. Story points work well for teams that want to focus on relative effort and avoid false precision. Hours work well for small, well-defined tasks where the team has strong historical data. The best choice depends on the team's maturity, the type of work, and how estimates are used in planning. Consistency matters more than the unit.

How do you handle estimates for work the team has never done before?

Use analogies and spikes. An analogy compares the unknown work to the most similar completed work. A spike is a time-boxed investigation, typically one to two days, where a developer explores the unknown area enough to produce a more informed estimate. Both techniques acknowledge uncertainty rather than pretending it does not exist.

How often should teams recalibrate their estimation process?

At minimum, review estimation accuracy at the end of every sprint or iteration. A monthly or quarterly deeper analysis that examines trends across multiple sprints is also valuable. The goal is to identify systematic biases, such as consistently underestimating frontend work or overestimating infrastructure tasks, and adjust the process accordingly.