The Estimation Process

The approach we commonly use for the estimation is Planning Poker, a structural and commonly accepted method in the industry.

  • The business requirements are broken down into smaller pieces, or “stories”.
  • A group of 3–4 developers, including the tech lead, sit in a room with the project manager and business requirement analyst to discuss and estimate each story.
  • Every developer receives a set of “t-shirt size” cards that has “S”, “M”, “L”, or “XL” letters on them (sometimes Fibonacci numbers).
  • The analyst presents each story and all developers think about it and ask questions.
  • Once all the questions are cleared, the project manager will ask every developer to present their cards at the same time to get an estimate.
  • If they vary, they will discuss and reconcile the difference to an agreed letter/number. Each story may take an average of 2 to 5 minutes to estimate depending on its complexity.
  • The end result of this estimation exercise is a list of relative and comparable t-shirt size letters on each story.

Cognitive Biases Impact Estimation

However, there are a few issues with this approach that could lead to a biased outcome. To start with, as there is always a time constraint, to get a result in a short time frame, it somewhat forces the developers to use their intuition to make the estimation other than to use their system 2 thinking (Kahneman, 2011). Prior to walking to a meeting room, the developers usually do not have any knowledge of the details of the business requirements they were about to estimate. Although they can ask questions, in 2 to 5 minutes it is hard to gain an in-depth understanding, and they have to make decisions based on what the analyst presented to them. If there is a push to get smaller estimates, which is usually expected by the project stakeholders, the team may take the expectation as an anchor and create underestimated results.

Approaches To Reduce The Biases

Simple Approaches

The estimation process has a built-in philosophy, “Consistency” and “Comparable” principles, to reduce the biases. To ensure them, at the end of the estimation, the project manager should list all estimated stories and group them by their estimated categories. They can ask the team to do a sanity check to evaluate if all the stories within the same “estimated bucket” are roughly the same size and have similar risks. Outliers should be discussed and reestimated.

Linear Regression Bootstrapping

To improve the estimation accuracy, a linear regression bootstrapping approach could be applied. Developers have some criteria in mind when they estimated the sizes of stories. We can reveal these criteria as variables and use them to find the predicted values based on a regression model.

  • development effort
  • whether the story involves third-party integration
  • testing complexity
  • communication
  • collaboration needs with another team
  • and the team’s familiarity with the technology.

Monte Carlo Simulation

Monte Carlo simulation is another effective approach to estimating uncertainties and reducing biases. (I will write details in another article). Monte Carlo simulates the estimation with random sampling from the historical estimates, and with the large sample size, the estimated size of the project will form a bell curve. Then the managers can choose a confidence level to manage or accept the risk.


  • Fox, C. (2014). Managerial Decision Making, Anderson School of Business
  • Kahneman, D. (2011). Thinking, Fast & Slow (TF&S)
  • Lovallo, D. & Kahneman, D. (2003) Delusions of Success: How Optimism Undermines Executives’ Decisions. Harvard Business Review, 56–63
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. science, 185(4157), 1124–1131.



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