Cognitive Biases in Agile Estimations and How to Avoid Them

7 minute read

Cognitive biases, often unconscious influences on human judgment and decision-making, can significantly distort the accuracy of Agile estimations. These biases, like invisible bugs in our mental software, can lead to errors that affect the outcomes of project planning. Whether it’s anchoring to the first piece of information heard or overly optimistic predictions about project timelines, they can subtly undermine the efficiency of even the most experienced Agile teams.

In Agile software development, estimation is a critical process that determines project timelines, resource allocation, and, ultimately, the success of a project. The accuracy of these estimations is important but frequently jeopardized by the human propensity to cognitive biases. As teams increasingly implement “Sprint Poker,” a blind team-based estimation game for gauging task complexity, recognizing and addressing these biases becomes crucial.

Let’s move from theory to practice and see how these biases play out during a typical round of Sprint Poker, starting with one of the most prevalent: anchoring bias.

Common Cognitive Biases in Agile Estimation

When we gather around our virtual tables for a round of Sprint Poker, we’re not just dealing cards and choosing numbers; we’re also playing against some invisible opponents—cognitive biases. These biases are the mental shortcuts and patterns that can skew our perception of how much work a user story entails.

Anchoring Bias

Picture this: you’re at the start of a “Sprint Poker” round, and someone tosses out an estimate. That number? It’s sticky. It sets the tone for the whole game, and like a catchy song, it’s now the tune everyone’s humming. This is anchoring bias at play. It’s not just a number; it’s the starting block that every other estimate will measure itself against, whether it’s a fair comparison or not. The danger here is that the anchor might not be the most accurate number, but it’s got a way of making everyone’s guess gravitate towards it, throwing off the whole estimation dance we’re trying to get right.

While anchoring bias can cause our estimations to stick to the first number heard, the planning fallacy takes us down a different but equally treacherous path of unwarranted optimism.

Planning Fallacy

The Planning Fallacy convinces us that, sure, we can fit in one more task in the sprint and that complex feature? Add it as well! It’s like planning to run a marathon after a week of couch potato life. Our brains gloss over the challenging parts, focusing on the finish line without considering the blisters we’ll get along the way. This bias has us underestimating the time and effort needed, leaving us sprinting (and not the Agile kind) to play catch up when reality checks in.

Just as the planning fallacy may lead us to underestimate our challenges, confirmation bias ensures we ignore evidence contradicting our optimistic plans.

Confirmation Bias

Confirmation Bias is the sneakiest of the three that we’ll talk about today. It’s when we go on a scavenger hunt for evidence that backs up our initial estimates and conveniently overlook anything that doesn’t. If a task is a walk in the park, we’ll ignore the park’s closing times, the weather forecast, and the fact that we might need to run. This selective vision can cause us to miss valuable insights, leading to a sprint plan that’s more of a wish list than a roadmap. It’s like setting out on a road trip with a map showing only the sunny skies, not the storms.

Having navigated the tricky waters of cognitive biases, let’s now focus on the tools and techniques that can help us steer clear of these hidden obstacles.

Tools and Techniques to Mitigate Cognitive Biases

Now that we’ve highlighted these sneaky cognitive biases let’s discuss how we can dodge them. To combat the sticky influence of anchoring bias, the Delphi Method emerges as a powerful equalizer in the game of Sprint Poker.

The Delphi Method

The Delphi Method may sound like some arcane ritual, but in Sprint Poker, it’s as common as the daily stand-up. In essence, it’s a method of group communication that involves a series of rounds of anonymous voting — a feature you’ve likely used in Sprint Poker without even realizing it’s a technique with a name.

Each team member provides their estimate without the influence of the rest of the group. It’s just you and your judgment, untainted by the others. This is where the magic happens. Because when all those individual estimates are revealed, they’re not just a scatterplot of wild guesses. They’re informed, independent assessments that haven’t been swayed by the first figure thrown into the ring.

The Delphi Method leverages the collective intelligence of the team, and this collective wisdom is further enriched when we draw on the strengths of a diverse team composition.

Diverse Team Composition

Diverse team composition isn’t just about bringing different faces to the table; it’s about leveraging the power of cross-functional Agile teams to create a more accurate scoring system in Sprint Poker. With team members hailing from various disciplines—developers, testers, UX designers, and product owners, each person brings their unique expertise and viewpoint to the estimation process.

As we assemble our toolkit to counter cognitive biases, we turn to one of the most data-driven tools in our arsenal: Reference Class Forecasting.

Reference Class Forecasting

Reference Class Forecasting is where we bring in the heavy hitters – historical data. By comparing our current stories to similar past stories, we can ground our estimates in reality, not just optimism. It’s not magic, though; it’s method. By digging into the archives and pulling up the stats on how we’ve performed in previous sprints, we set a realistic baseline for our current project’s user stories.

With proactive measures in place, we must also look back to learn and improve, making retrospectives an indispensable part of our journey towards accurate estimations.


Retrospectives are our rearview mirror. They provide critical insight into the road we’ve traveled and the bumps we’ve hit. They’re a deep dive into the successes and fumbles of our past sprint. By carving out time for this reflection, we turn every project, every sprint, and every story point into a learning opportunity.

Through the lens of these techniques, from the Delphi Method to the reflective power of retrospectives, we see a clear path forward. Let’s summarize our journey and look ahead to the continuous improvement of our estimation practices.

Challenges and Considerations

Tackling cognitive biases is no small feat; they’re hardwired into our thinking. Every estimation session is a new battle against these biases, requiring us to be ever-aware and diligent. It’s about cultivating a mindset that constantly questions and challenges assumptions, that looks beyond the surface numbers and digs into the “why” behind each estimate.

The trick is in setting up guardrails that keep these biases in check. It means creating an environment where it’s safe to speak up and question estimates, no matter how experienced the estimator is. It’s about fostering a team culture that values data over gut feelings and one that sees the wisdom in pausing and reflecting rather than rushing to a consensus. It isn’t about slowing down the process; it’s about making sure the path we’re choosing is the right one.


Throughout this exploration, we’ve unpacked the subtle yet significant ways cognitive biases can skew our Agile estimations. From the anchoring bias, which sets a deceptive baseline, to the planning fallacy and overly optimistic projections, we’ve uncovered how these hidden forces can play havoc with our best-laid plans. Along the way, we’ve navigated the common pitfalls of Agile estimation and introduced some robust strategies to counteract them, like The Delphi Technique, leveraging diverse team compositions, employing Reference Class Forecasting, and conducting insightful retrospectives.

Now, as we round out our exploration, it’s clear that the path to more accurate Agile estimations isn’t a one-and-done deal. It’s a continuous journey of learning and adaptation, an approach that Sprint Poker tools like Parabol can help us navigate. But the true north of this journey isn’t found in our tools; it’s in our mindset, our readiness to recognize our biases, and our commitment to refine our estimation practices, sprint after sprint.

Let’s not just walk away with a deeper understanding of cognitive biases; let’s step forward with a renewed commitment to bringing our most insightful, unbiased selves to every estimation session. Because at the end of the day, it’s not just about the numbers we jot down. It’s about fostering a culture of accuracy, transparency, and continuous improvement that defines the excellence of our Agile practice.