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copied!<p>The simple truth is that <strong>estimation accuracy</strong> is a contradiction in terms. Like unicorns, it simply doesn't exist. By definition, an estimate is <strong><em>not</em></strong> accurate.</p> <p>With that in mind, Scrum and other agile methodologies try to work around that limitation, as opposed to beating down windmills. In Scrum, an <strong>a priori</strong> estimate of the <em>complexity</em> of <strong>Product Backlog Items</strong> (user stories, requirements, etc.) is made to give the product owner a <strong>rough</strong> idea of how many stories he can expect to be completed in the upcoming sprint. After breaking down the PBIs into tasks, each task is estimated according to the time they <strong>believe</strong> it will take to complete. Once the team's capacity is met, they have a (slightly) better estimate of what they can deliver by the end of the sprint. </p> <p>These estimates are <strong><em>still</em></strong> not accurate.</p> <p>The way an agile product owner deals with this inaccuracy is by reducing the <strong>cost of delay</strong> in delivering the product. The PO defines and prioritizes his stories in such a way that he delivers the most important part of the product as early as possible, and creates a (still incomplete) usable and valuable product as early as possible. This way, whatever is not done on time (end of sprint or release date) is still the best product that <strong>could have been delivered</strong>, and often a good enough release can be created <strong>before time</strong>, with the rest, the <strong>least important features</strong> delivered in small batches, soon after.</p> <p><em><strong>That</em></strong> is how Scrum deals with estimates' (in)accuracy.</p>

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