Planning Poker deck by Rachmaninoff, licensed under the Creative Commons Attribution-Share Alike 4.0 International license.
Having experienced good and bad Scrum practices, I would like to help you understand why you really want to follow all steps of Planning Poker and use the right tools, specially when you run a distributed Scrum team.
When you estimate user stories for a Scrum sprint, you want to estimate as accurately as possible given the information you have. Inaccurate estimates are a problem because they may cause you to drop stories (which is bad for business) or to carry through more work than you expected (which is bad for you).
When you choose to just say your estimate out loud after discussing a story instead of following all steps of Planning Poker, your estimates may be inaccurate for at least two reasons. The two reasons affect you even more when you run a distributed Scrum team. Let me explain the reasons and why I believe you are better off estimating by Planning Poker.
Reason 1: Team members may be biased
Team members may be biased away from an accurate estimate because when you say your estimate out loud, at least anchoring bias or peer pressure may happen. When either happens, you take into account information that is irrelevant to your estimate and thus produce an inaccurate estimate. Anchoring bias and peer pressure are phenomena that are very difficult to overcome once they happen.
Anchoring bias happens when you learn the estimate of another team member before you select your own estimate. Anchoring bias consists in that you select an estimate that is similar to the estimate that you just learned. Anchoring bias is very difficult to overcome because it is unconscious and neither sheer will nor expertise are effective against it. Consider the following three experiments.
Experiment 1. Anchoring happens unconsciously. Wilson, Houston, Etling, and Brekke applied the following experiment (Study 1 in their report) to a number of psychology students. They found that anchoring happens unconsciously when you have in mind a number even if that number is irrelevant to the estimation that you are about to do and nobody asks you to compare that number to whatever you are estimating. In the experiment, each participant received a number. Each participant was told that the number was random when in reality everyone received the same number. Some participants were asked to indicate if they believed that the number was lower, greater or equal to the number of countries in the United Nations. The rest were asked to compare the number with the number of physicians in their local phone book. Finally, all participants were asked to estimate the number of countries in the United Nations. The analysis of experimental results indicates that estimates are biased towards the given number regardless of whether participants compared that number to the question that they estimated or not.
Experiment 2. Anchoring happens even if you try compensate. Wilson, Houston, Etling, and Brekke applied the following experiment (Study 5 in their report) also to a number of psychology students. They found that anchoring is very difficult to counter even when you are warned that anchoring will happen. The experiment compared two conditions. One condition consists in giving the same anchor to each participant, asking them to indicate if the number was lower, greater or equal to the number of physicians in the local phone book, and asking them to estimate the precise number of physicians. The other condition is the same as the first except that participants where explained that anchoring bias would occur and that they should avoid it. The analysis of the experimental results indicates that there is no significant difference between the biases given by the conditions.
Experiment 3. Anchoring bias happens even if you are an expert. Englich, Mussweiler, and Strack did the following experiment (Study 1). They found that an anchor that is irrelevant from the legal point of view influenced the sentencing decisions of legal professionals. Participants were legal professionals that were either judges or had been judges at some point. Participants were asked to consider realistic case material about an alleged rape from the point of view of a criminal judge. Afterwards, participants were exposed to a potential sentence via a questionnaire. The questionnaire asked that participants imagine a situation where a journalist interviews them and introduces the potential sentence. Experimental results indicate that participants were biased by the potential sentence suggested by the journalist.
Peer pressure consists in that the majority of estimates that other members announce are the same. Peer pressure is difficult to overcome because even when you have reason to stick to a particular estimate, you may still choose the estimate that the majority announced. Consider the following experiment.
Experiment 4. Peer pressure can be more powerful than facts. The experiment is by Solomon Asch (Experiment I in his report). He found that for a clear matter of fact, people under peer pressure give inaccurate estimates way more often than when they are not under peer pressure. He also found that under peer pressure, the majority of people will eventually give you an inaccurate estimate. The experiment is the following. The experiment happened in a classroom. Each run of the experiment consisted of 18 trials. Each trial consisted in matching one line drawn on a card (the standard) with one of three other lines on another card. In each trial, the corresponding pair of cards was placed on the ledge of the blackboard. For each run of the experiment, 8 people participated. One of them was the subject and the rest were collaborators with Solomon. In each trial, each participant was asked to announce publicly their estimate. The subject was always second to last in announce his estimate. For each run of the experiment, the estimates given by the collaborators followed the same pattern. Collaborators estimated correctly in the first two trials and then estimated 12 of the remaining trials unanimously and incorrectly. Those 12 trials are the critical trials. In between the critical trials, collaborators estimated correctly the remaining 4 trials. The control experiment was the same except that participants did not announce their estimates publicly. The experimental results are the following. Without peer pressure, subjects estimated correctly 99% of the critical trials and thus Asch concludes that the differences between the standard line and the rest were clearly distinguishable. Under peer pressure, estimates were correct 63% of the time and for this reason Asch concludes that peer pressure has a significant effect on estimates. Without peer pressure, 5% of subjects gave some incorrect estimate. Under peer pressure, 76% of subjects gave some incorrect estimate. Thus, a given person that is under pressure will probably give you a bad estimate at some point.
You are better off estimating with Planning Poker because Planning Poker rules out anchoring bias and peer pressure. The reason is that Planning Poker requires that each team member privately selects their estimate (step 3) and that all estimates are revealed simultaneously (step 4). This way team members do not know the estimate of any of the other members when estimating.
If you are not sure that there is a point in hiding estimates after the first estimation round, consider the way I see the situation. Once the team discusses the estimates they chose for a story (in step 6), that discussion and the estimates they chose become relevant information for the second round (when they go back to step 3). The same is not true for the estimates that will be produced in the second round. The estimates produced in the second round are irrelevant because they correspond to somebody else’s appreciation of the story. Thus, Planning Poker rules out anchoring bias and peer pressure in all estimation rounds.
Reason 2: Team members may not really understand the story
When team members say their estimates out loud, you can pick one of the previous estimates to try to get away with not justifying your choice. Trying to get away with not justifying your choice is unacceptable even if the reason is that you are trying to avoid being embarrassed because you do not really understand the story.
In my experience, even people that would understand stories during Planning Poker do miss the opportunity to understand stories when Planning Poker is not applied. I believe that there are two ways of explaining why these team members understand better by Planning Poker.
Explanation 1. One explanation is that without estimates to copy, the next best thing team members can do to avoid embarrassment is to actually understand the story.
Explanation 2. The other explanation is that Planning Poker makes you responsible for your estimate. The reason is that Planning Poker substitutes the impression that estimating is a group activity with the impression that estimating is an individual activity. You are motivated because taking responsibility for your work is rewarding to you.
I like Explanation 2 better because I feel that, out of the two explanations, it is the most constructive reason why you are better off estimating by Planning Poker. I agree with Rubin when he says that the majority of the value of Planning Poker is the discussion and better understanding of user stories that Planning Poker fosters.
When you estimate, you should be really using Planning Poker cards. If you do not have cards, try using a mobile app. If you do not have smartphones, give everyone pen and paper and tell them to write their estimates there.
When you are running a distributed team, do not just write your estimates in a chat one after the other. Ask people to use a dedicated tool, like my own spoker-master.
Really, there is no excuse for saying your estimates out loud.
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I love to explain and answer questions on programming problems, the kind you find in coding interviews. I publish a new programming problem and its solution every month. Did I mention that I love to answer questions?
Kenneth Rubin explains in his book Essential Scrum: A practical guide to the most popular agile process that Planning Poker consists of the following steps.
- The product owner selects a user story to be estimated and reads the story to the team.
- Development team members discuss the story and ask clarifying questions to the product owner, who answers the questions.
- Each estimator privately selects a card representing his estimate.
- Once each estimator has made a private selection, all private estimates are simultaneously exposed to all estimators.
- If everyone select the same card, we have consensus, and that consensus number becomes the estimate of the story.
- If the estimates are not the same, the team members engage in a focused discussion to expose assumptions and misunderstandings. Typically we start by asking the high and low estimators to explain or justify their estimates.
- After discussion, we return to step 3 and repeat until consensus is reached.
- Asch, S. E. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority. Psychological Monographs: General and Applied, 70(9), 1-12. doi:10.1037/h0093718
- Englich et al.
- Englich, B., Mussweiler, T., & Strack, F. (2006). Playing Dice With Criminal Sentences: The Influence of Irrelevant Anchors on Experts' Judicial Decision Making. Personality and Social Psychology Bulletin, 32(2), 188-200. doi:10.1177/0146167205282152
- Rubin, K. S. (2012). Essential Scrum: A practical guide to the most popular agile process (pp. 129-133). Upper bSaddle River, NJ: Addison-Wesley.
- Wilson et al.
- Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A new look at anchoring effects: Basic anchoring and its antecedents. Journal of Experimental Psychology: General, 125(4), 387-402. doi:10.1037/0096-34220.127.116.117