The Impact of Cognitive Biases on Test and Project Teams

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5/7/2014 3:00:00 PM

The Impact of Cognitive Biases

on Test and Project Teams

Presented by:

Thomas Cagley

The David Consulting Group

Brought to you by:

340 Corporate Way, Suite 300, Orange Park, FL 32073

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Thomas Cagley


VP of consulting for The David Consulting Group, Thomas Cagley is an authority in guiding organizations through continuous process improvement. His areas of expertise encompass a wide variety of methods and metrics including lean software development, agile software development, quality integration, quality assurance, and the application of the Software Engineering Institute’s Capability Maturity ModelÂ® Integration (CMMI) to achieve process improvements. Thomas is an active member of the International Function Point Users Group and a Certified Function Point Specialist. He is the editor of the Software Process and Measurement Podcast (SPaMCAST), blogs at tcagley.wordpress.com, and can be reached at [email protected].

4/29/2014

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Measure. Optimize. Deliver.

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The Impact of Cognitive Biases on

Test and Project Teams

STAREast

Orlando, Florida

May 7, 2014

©2014 David Consulting Group

What Is Cognitive Bias

• Cognitive bias reflects a pattern of behavior in which a person

acts differently than would seem normal in certain situations

based on inaccurate judgment or illogical interpretation

• Cognitive biases work by causing an individual to perceive the

world around them in a manner that is outside of what normally

would be considered logical.

• Cognitive biases are neither good nor bad if we are aware of

them.

No man is an island,

Entire of itself,

Every man is a piece of

the continent,

A part of the main.

John Donne

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Why Is Cognitive Bias Important?

• Biases are a part of nearly every human interaction because

every human has cognitive biases and because humans are

all unique the impact of the bias are unique to each individual.

Cognitive biases are an inescapable part of basic human

nature.

• Project team members make decisions on continuous basis.

Most decisions are made based on how the decision maker

sees the information he or she has at hand.

• All biases can create blind spots. A good coach or leader will

first be aware of his or her biases and then help the team

understand their own blind spots. All while not abandoning the

shortcuts that can help us perceive what is important and

make timely decisions.


Where do Biases Come From

• Biases develop as shortcuts that help us perceive information

and help us make decisions quickly.

• Pattern recognition biases helped early humans stay alive by

recognizing situations where you’d likely run into predators.

The resulting decisions kept our ancestors alive, even if there

were false positives (you could have lots of false positives, but

only one false negative).

• Project teams (Agile or not) use or fall prey to a wide range of

biases that affect perceptions and impact decisions.

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Three Categories of Cognitive Biases

• Perception biases are filters and / or shortcuts that help us perceive information quickly in a manner that turns out to be a generally beneficial to a decision process. Perception biases affect how project teams see information and the types of decisions that can be made.

Perception Biases

• Behavior biases effect how we behave or how we tend to group together (which then affects how we perceive the world around us. Behavior biases create a feedback loop to help us to successfully interact with the environment (at least our perception of our own world).

Behavior Biases

• Motivational biases (also known as social biases and attribution biases) reflect errors make when evaluating the rational for your own behavior as well as others. Misperceptions of what is driving behavior can cause team communication problems and erode team trust.

Motivational Biases


Perception: Anchor Bias

• Anchor bias refers to the tendency to rely heavily on one

piece of information when making decisions. This bias is often

seen when early estimates for a project or tasks are made.

The instant they are placed on the table they become a

reference point to which all changes will be compared.

Impact Example(s)

1. Can you test this project in two weeks?

2. If I know if it is -12F I am going to feel cold no matter what it

says on the thermostat.

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Perception: Clustering Illusion

• Clustering illusion (or clustering bias) is the tendency to see

patterns in clusters or streaks in a smaller sample of data

inside larger data sets.

Impact Example(s):

1. Does a rash of .net coding errors mean programmers need

to be retrained?

2. If one project had 1000 regression test errors and another

100, which one had better performance?

3. Based on the fish in the picture are black carp rare?


Perception: Knowledge Bias

• The curse of knowledge bias generates a filter that blocks the

ability to think about a topic from a different and generally less

informed perspective.

Impact Example(s)

1. Your laptop got an update this morning and now it is slow.

Updates have been known to cause trouble before . . .

2. The cable cars typically takes 15 minutes to reach the top of

Sugarloaf, you have not seen a car in 30 minutes is the cable

car broken?

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Perception: Availability Cascade

• An availability cascade is when a concept becomes more and

more plausible the more it is repeated publicly. It is a self-

reinforcing feedback loop.

Impact Example(s)

1. Does the constant publicity on the topic of Agile entice more

organizations to try Agile?

2. Remember December 21st

and the Mayan Calendar.


Behavior: Zero-Risk Bias

• A zero-risk bias reflects a preference for mitigating (mitigating

means finding a way to make the risk go away) a small risk

down to zero, rather than mitigating a larger risk that you can’t

drive to zero.

Impact Example(s)

1. I can’t stop my customers from wanting to release before we

are fully tested but I can make sure I fully staffed.

2. I might be able to know what is happening but if I can’t do

anything about it, am I mitigating the right problem?

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Behavior: Bandwagon Effect

• The bandwagon effect occurs when there is a tendency to

adopt an idea because the crowd does. For example, when an

idea is shown on the cover of all the industry journals, teams

tend to take it up with gusto.

Impact Example(s)

1. The media (classic and new media) amplify ideas making

them seem like everyone is doing them (e.g. Agile, Lean,

CMMI, Six Sigma).

2. My mother always used to ask if my friends jump off the roof

would I follow them?


Behavior: Illusion of Control

• This bias is called the illusion of control, which is defined as

the tendency to overestimate one’s (or a team’s) degree of

influence over external events.

Impact Example(s)

1. Many test managers believe they can make up for getting code

from the developers late.

2. Do you ever turn off a football game so your team will win?

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Behavior: Social Desirability Bias

• The social desirability bias is the tendency to over report

desirable behaviors while under reporting undesirable

behaviors.

Impact Example(s)

1. Why are projects green status today and then red

tomorrow?

2. Why do people happy to live in large cities despite horrible

pollution?


Behavior: Comparison Bias

• When a team is assembled by a leader with a social

comparison bias, membership decisions are made so that

those who are on the team don’t compete with the leader’s

strengths.

Impact Example(s)

1. Team diversity leads to innovative solutions, homogenous

teams tend to be weaker.

2. Bull elephants drive other males away that can compete.

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Motivation: Halo Effect

• The halo effect is when our impression of a person influences

how we interpret their specific traits.

Impact Example(s)

1. Until late in his career very few people were able to

“perceive” the changes in Barry Bonds.

2. Mark C. Bojeun, author of , suggests that leaders create a

bubble around teams that can empower high performance

teams.


Motivation: Illusion of Transparency

• Illusion of transparency is a bias in which an individual

overestimates another individual’s ability to know them, and/or

overestimate their own ability to understand what is driving

someone else.

Impact Example(s)

1. Johari Window indicates that there is always part of a team that

we do not understand.

2. Teams, like dance partners, only think they know how their

partner will react.

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Motivation: Intergroup Bias

• Hardening of team boundaries can lead to intergroup bias.

Intergroup bias motivates members of a group to give

preferential treatment to others members of the group.

Impact Example(s)

1. The Stockholm effect is a type of intergroup bias.

2. Teams resist ideas that are outside the teams norms, consider

the difficulty integrating testers into Agile development teams

from independent testing teams.


Motivation: Fundamental Attribution Error

• Fundamental attribution error refers to a scenario in which an

individual overemphasizes personality-based explanations for

behaviors (e.g. they are lazy, they aren’t very smart) in others

while underemphasizing the influence the situation had on

driving the behavior.

Impact Example(s)

1. How many times have you heard, “developers never give us

enough time to test because they don’t understand testing?”

The real issue may be that their schedule is just as crunched

as the test team.

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Class Discussion Example 1

Rumadak’s manager tells he believes the new SAP release can be

system and regression tested in before she estimates the project.

1. What bias is play in this circumstance?

2. What should Rumadak do to guard against this form of bias?

3. When can we use this type of bias to our advantage



Joe is a both a top tech lead and leader of the organizations

standards committee. Joe is leading a move to disband the

independent test group and incorporate the personnel into

development.

1. What types of bias might Joe bring to the discussion?

2. What significant bias will Joe’s suggestion trigger in the independent test group?

3. What biases could be used to resist the pressure to incorporate?

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Billy needs to recruit a team member. Team members that get more

attention than he does makes Billy nervous. Therefore, in

interviewing he is careful to search for people that, while they can do

the job, will not outshine him.

1. What type of bias(es) Billy showing?

2. How can this type of behavior impact Billy’s team and the people on his team?

3. If you were Billy’s manager how would you help Billy improve the recruitment process and

reduce the effects of his bias?


Summary

• Biases are everywhere.

• Everyone is effected by cognitive biases.

• Not all biases are bad.

• Just like our actions, we are responsible for our biases.

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Questions . . .

Tom Cagley, CFPS, CSM

VP of Consulting


[email protected]

(440) 668-5717

Software Process and Measurement Podcast

http://www.spamcast.net (or iTunes)

Software Process and Measurement Blog

http://tcagley.wordpress.com

Thomas Cagley


VP of consulting for The David Consulting Group, Thomas Cagley is an authority in guiding organizations through continuous process improvement. His areas of expertise encompass a wide variety of methods and metrics including lean software development, agile software development, quality integration, quality assurance, and the application of the Software Engineering Institute’s Capability Maturity ModelÂ® Integration (CMMI) to achieve process improvements. Thomas is an active member of the International Function Point Users Group and a Certified Function Point Specialist. He is the editor of the Software Process and Measurement Podcast (SPaMCAST), blogs at tcagley.wordpress.com, and can be reached at [email protected].

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Cognitive Bias and Effective Teams

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Software Process and Measurement Cast

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March 2014

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mailto:[email protected]


Cognitive Bias and Effective Teams

Thomas M Cagley Jr

Meghan E Cagley

No man is an island,

Entire of itself,

Every man is a piece of the continent,

A part of the main.

- John Donne

John Donne may not have had project teams and cognitive biases in mind when he wrote his famous

poem; however the description is apt. Cognitive bias reflects a pattern of behavior in which a person acts

differently than would seem normal in certain situations (based on inaccurate judgment or illogical

interpretation). Amos Tversky and Daniel Kahneman introduced the phrase cognitive bias in 19721. Biases

can affect how we make decisions, how teams and individuals behave and even our perception of

ourselves. Biases are a part of nearly every human interaction. Every human has cognitive biases and

because humans are all unique the impact of the bias are unique to each individual. Cognitive biases are

an inescapable part of basic human nature. In order for a team to effectively deliver business value, it

must recognize the biases each member brings to the table and how they interact.

Cognitive biases work by causing an individual to perceive the world around them in a manner that is

outside of what typically would be considered logical. For example, when someone falls prey to an anchor

bias when buying a car they will be influenced by the first price they are exposed to even if it does not

match their expectations. A company has to advertise a regular price before they can establish value or

quote a regular price. For example, many people believed that setting a high price for the original Iphone

then dropping the price substantially 74 days later was an attempt to use the anchor bias to create a

perception that the IPhone was a good value for the money. Our cognitive biases are neither good nor bad

if we are aware of them.

Project team members make decisions on a continuous basis. Most decisions are made based on how the

decision-maker sees the information he or she has at hand. One bias that can affect how information is

perceived is the illusory correlation. The illusory correlation is when you think a relationship exists

1 Kahneman, Daniel; Shane Frederick (2002). "Representativeness Revisited: Attribute Substitution in Intuitive

Judgment". In Thomas Gilovich, Dale Griffin, Daniel Kahneman. Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge: Cambridge University Press. pp. 51–52. ISBN 978-0-521-79679-8.


http://en.wikipedia.org/wiki/International_Standard_Book_Number

http://en.wikipedia.org/wiki/Special:BookSources/978-0-521-79679-8


between two or more variables, but that relationship doesn’t necessarily exist. For example, a team that

works more hours a week might be perceived to have higher productivity because working longer gives the

perception of creating more output. But, of course, hours and productivity aren’t necessarily related. Once

you fixate on one relationship, you will often pay less attention to others. In this case, you’ll miss the level

of effort the team is expending, which if you are paying for the work by the hour means you could run up

quite a bill when you think your productivity is good based on hours spent. These biases can impact the

outcome of decisions of course.

Biases develop as shortcuts that help us perceive information and help us make decisions quickly. Pattern

recognition biases helped early humans stay alive by recognizing situations where they’d likely run into

predators2. The resulting decisions kept our ancestors alive, even if there were false positives (you could

have lots of false positives, but only one false negative because the one false negative would get you eaten).

Project teams (Agile or not) use or fall prey to a wide range of biases that affect perceptions and impact

decisions. A sample of common biases include:

Anchor bias refers to the tendency to rely heavily on one piece of information when making decisions.

This bias is often seen when early estimates for a project or a tasks are made. The instant they are placed

on the table they become a reference point to which all changes will be compared.

Clustering illusion (or clustering bias) is the tendency to see patterns or trends in clusters or streaks in

a smaller sample of data inside larger data sets. For example, a team I recently worked with had an

average velocity of 30 story points per sprint (ranged between 20 – 36). They had three sprints in a row

that delivered 40+ story points. While nothing significant had changed about how the team was working,

outsiders saw a pattern and believed something out of the ordinary was occurring. (FYI – if there is no

statistical significance to the data what we are seeing is “common cause” variance3.)

The curse of knowledge bias generates a filter that blocks the ability to think about a topic from a

different and generally less-informed perspective. In many cases being an expert on a topic makes it very

difficult to see an out-of-the-box solution. This is one of the reasons significant changes in IT platforms or

concepts come as new players with less experience enter the organization. Similar to the curse of

knowledge bias is the status quo bias, which is the tendency to want things to stay relatively the same. It

creates a perception filter that limits the individual or team to data and concepts that make them

comfortable. These two are similar because knowledge and the status quo are linked.

2 http://www.slideshare.net/elentini1976/the-portable-mba 3 Common cause variance is a reflection of the capacity of the process rather than any special situation.


http://www.slideshare.net/elentini1976/the-portable-mba


An availability cascade is when a concept becomes more and more plausible the more it is repeated

publicly. It is a self-reinforcing feedback loop. Perhaps that is why Pokemon became more popular the

more it was shown on the Cartoon Network. Daniel Pink, author of To Sell Is Human, in a July 9th

Salesforce.Com webinar pointed out that repetition increases process fluency, which makes the repeated

item perceived to be more true. Sales, marketing and 24-hour news channels understand and use the

availability cascade bias to great effect.

A final example of biases that affects behavior and perception is optimism bias. Optimism bias is the

tendency to be overly optimistic about favorable outcomes. This bias is often exhibited in status reports or

in promises made early in a project. These are generally not intentional lies, but rather, because of our

optimism bias, the belief that we can that we can deliver more than is truly possible. Dr. Ricardo Validri in

Software Process and Measurement Cast 84 suggests that optimism bias is one of major reasons IT

personnel are poor estimators.

All biases can create blind spots. A good coach or leader will first be aware of his or her biases and then

help the team understand their own blind spots and at the same time not abandoning the shortcuts that

can help us perceive what is important and make timely decisions.

Biases can affect behavior. Neglect of probability is a type of cognitive bias common in IT organizations’

planning and estimating projects or in risk management. Neglect of probability creates a perception of

certainty. For example, most estimates are not certain and should be represented as a range based on

probability. Techniques like Monte Carlo analysis can be used to generate these estimates. However,

almost all estimates are represented as a single number and, as a result, we ignore the continuum of

probability. Lottery ticket sales are a classic example of probability neglect bias; buying one or 10 doesn’t

materially affect the probability of winning, but it does not stop those who think buying ten tickets

increases their chances of winning. In both cases, neglecting probability affects how we behave and make

decisions. Many of the filters and shortcuts that we develop to help us to successfully interact with the

environment can negatively impact team effectiveness. A sample of common biases that affects IT teams

in this category include the following 6:

A zero-risk bias reflects a preference for mitigating (mitigating means finding a way to make the risk go

away) a small risk down to zero, rather than mitigating a larger risk that you can’t drive to zero. This bias

can be seen when developing difficult, and therefore more risky, user stories. For example, a team will do

the easy pieces of work early in a sprint, leaving the more difficult work until late in the sprint which

increases the likelihood of not completing stories during an iteration. By leaving the difficult portions of


http://traffic.libsyn.com/spamcast/SPaMCAST_84_-_Valerdi_Change_and_Estimation_Outsourcing.mp3

http://www.nytimes.com/2013/08/10/your-money/win-a-lottery-jackpot-not-much-chance-of-that.html?_r=0

http://www.nytimes.com/2013/08/10/your-money/win-a-lottery-jackpot-not-much-chance-of-that.html?_r=0


work until later, we increase the chance we will miss our commitment when something goes wrong or that

the estimate will be wrong with no time to recover.

The bandwagon effect occurs when there is a tendency to adopt an idea because the crowd does. For

example, when an idea is shown on the cover of all the industry journals, teams tend to take it up with

gusto. When I was a child I used the “everybody is doing it” refrain to try to convince my mother that

staying out all night was a good idea - but she did not buy it. The bandwagon effect is more common with

teams that exhibit ideological homogeneity, because there are fewer alternative perspectives to provide

resistance. Where a common core belief is strongly held, groupthink can make adopting new ideas more

difficult to adopt.

I am firmly convinced that if I watch my alma mater play football on television they will lose (however, my

wife suggests that I am not that powerful). This bias is called the illusion of control, which is defined as

the tendency to overestimate one’s (or a team’s) degree of influence over external events. Teams or team

members who fail prey to the illusion of control can make poor decisions because they think they can

control the future.

The social desirability bias can be problematic, especially during retrospectives. The social desirability

bias is the tendency to over-report while under-reporting undesirable behaviors. Teams or individuals

that fall prey to this bias have a hard time identifying and dealing with the tough interpersonal issues that

can occur on teams. When this bias is present in a team, the coach or leader needs to address it directly by

focusing the team on the behaviors driving the delivery of value, rather than focusing on socially desirable

behaviors alone or risk deep-seated dysfunction.

Team composition is important. Most of us would agree that team membership should have a broad set of

capabilities and that members should push each other intellectually. When a team is assembled by a

leader with a social comparison bias, the leader should be sure that membership decisions are made

so that those who are on the team don’t compete with the leader’s strengths. This type of bias (and there

are a number of biases with similar impacts) leads to teams that will rarely challenge the leaders

perception of the status quo.

Biases drive behaviors. When biases generate unproductive behaviors the effectiveness of the team and its

members will be reduced. When the biases drive behaviors that are benign, a coach or leader can help sort

out problems. However, some biases generate behavior that is far from benign. Where a bias is causing

serious team issues, I strongly suggest involving a trained human relations specialist.



Motivational biases (also known as social biases and/or attribution biases) reflect the errors we make

when evaluating the rationale for both our own behavior as well as the behavior of others. Misperceptions

of what is driving behavior can cause communication problems among team members and erode team

trust. For example, a self-serving attribution bias occurs when success is attributed to internal factors and

failures to external factors. This type of bias can occur at the individual or the team level. While

attribution bias can improve self-esteem (or team-esteem), it can also cloud judgment by causing an

overestimation of capability. For example, if a team is able to deliver more than their long-term

productivity or velocity would predict, the team might believe that they have increased their capability to

deliver. If there haven’t been any fundamental changes, such as an infusion of knowledge, training or new

tools, the higher velocity may not be attributable to the team. A good coach will help teams examine these

types of biases during retrospectives.

A sample of common motivational biases that affect IT teams include:

The halo effect is when our impression of a person influences how we interpret their specific traits.

Recently, I observed a discussion between a very charismatic .net coder and a network administrator. The

coder’s charisma increased the weight of his argument. As the result, the network administrator agreed to

approve a new set of transactions for production that he probably should not have. Later, that approval

had to be renegotiated. The network administrator was motivated into a poor decision by the

programmer’s charisma.

Illusion of transparency is a bias in which an individual overestimates another individual’s ability to

know them, and/or overestimate their own ability to understand what is driving someone else. It is an

inescapable fact that humans interpret each other’s behaviors and actions, assigning a rationale to each

action and reaction which feeds how they act. Conflict can be motivated when interpretations of behaviors

are wrong and conflict can lead to reduced productivity.

Teams are a core feature of most modern IT organizations. By definition, all teams have a boundary.

Hardening of team boundaries can lead to intergroup bias. Intergroup bias motivates members of a

group to give preferential treatment to other members of the group. An overly hardened team boundary

can motivate a team to resist new ideas being introduced which leads to intellectual atrophy. Coaches and

leaders should encourage group cohesion but help teams avoid severe intergroup bias.

Fundamental attribution error refers to a scenario in which an individual overemphasizes

personality-based explanations for behaviors in others (e.g. they are lazy, they aren’t very smart) while

underemphasizing the influence the situation had on driving the behavior. Fundamental attribution error

assigns a theory of motivation to someone elses behavior (many times wrong) which then motivates the



assignor to incorrect behavior. When fundamental attribution error rears its ugly head on any team, a

coach, Scrum Master or leader should uncover the problem, usually in a retrospective, and facilitate a

resolution. When the bias is deep-seated, it may be necessary to leverage professional human resource

facilitators or ultimately to change the team composition.

Cognitive biases can affect projects in every step in which humans are involved. The impact of cognitive

biases tend to be clustered around how people behave and how they communicate. Cognitive biases are

not just psychological theory. As an example of the impact of cognitive biases on real life, use a typical

planning meeting to provide examples of how common cognitive biases can impact an Agile project.

A typical Scrum planning session is a two-step affair. In first step, the product owner starts by selecting

the work he or she wants to accomplish in the current sprint from the prioritized backlog. The product

owner is acting based on business value and input from the team and other stakeholders. While the

selection is made based on an informed process, the mere fact that an amount of work has been placed on

the table leverages the anchor bias and sets an expectation of what is needed. In the second step, the team

will negotiate with the product owner about what really can be accomplished with the amount work that

the product owner has suggested in mind. The discussion that occurs during the first step of the process

can be impacted by the cognitive biases of each individual participating. The types of biases can range

from the halo effect (overweighting the input from an individual based on their personality attribute) to

intergroup bias (ideas constrained by the team’s boundaries). In scenarios where the product owner and

one or more senior team members agree, many times the entire team can be pulled into agreement using

the bandwagon effect. Coaches need to ensure that the backlog is prioritized well and that the product

owner’s initial selection is sensitive to the team’s capacity in order to minimize the impact of anchor and

other cognitive biases.

During this second step in the planning process, the team breaks down the work and determines how

much they really can accomplish. The team begins with an understanding of what they are being asked for

(the other side of the anchor bias). Once they have the list from the Product Owner they can begin to plan

(identify, order and size tasks). The planning process is apt to be affected by many of the planning and

motivational biases noted during the first step of the process and others that are more prevalent in IT

personal. For example, IT personnel are trained problems-solvers and generally have not met a problem

they do not think they can solve (which leads to optimism bias which is the tendency to be overoptimistic

about favorable outcomes). This can lead a team astray when addressing unique business or technical

problems. Another bias that impacts planning is the planning fallacy. The planning fallacy is the tendency

to underestimate task completion times - remember all those late nights late trying to get things

done. The power of group planning sessions is that many minds can be brought to bear on the topic.



Assuming that the team has not fallen prey to intergroup bias, a diverse team will be able to avoid many of

the biases that plague individuals. During the team component of the planning process the Scrum Master

or coach should observe how the team is interacting and facilitate the team’s interactions.

The discussion of cognitive biases is not a theoretical exercise. Even the relatively simple process, sprint

planning in Scrum, is influenced by the cognitive biases of the participants. Biases are powerful

psychological filters that affect how both individuals and teams perceive the world around themselves and

then how they behave. Biases reflect shortcuts in how we interpret and react to stimulus. In many cases

these reactions are valuable; however, they can also cause problems as many shortcuts can.

Understanding how biases impact the way that individuals and teams perceive the world around them can

help teams make better decisions and therefore deliver value more effectively. When teams are

established, members begin the process of learning each other’s biases and capabilities. No amount of

team-building exercises can replace the knowledge gained by collaborating and creating value for the

organization. This learning curve is one of the reasons it is critical for teams to stay together over time.

As teams build an internal knowledge base and accumulate experience, not everything that is discovered

will be conducive to team effectiveness. Assuming that the issues are not pathological (e.g. ax murders or

chronic liars), the team should use techniques such as periodic retrospectives to surface problems and

deal with them. For example, one team I was observing had been struggling with a member that tended to

dominate discussions based on his significant technical prowess in a specific technology. More junior

members were routinely cut out of conversations. The issue surfaced during a retrospective. The

technologist in question agreed that his dominance was causing a communication issue and that he would

be part of the solution. The team decided to adopt a signal technique, i.e. a safe word, to cut him off and

let others get into the discussion. The team dealt with problem without having to get managers involved.

The Scrum Master and/or coach should facilitate the process but should not solve the problem for the

team.

Effective teams build an understanding of the capabilities and biases of each member and the team as

whole. The most effective means of a team learning about itself is by working together towards a common

goal. As teams work together, techniques such as retrospectives provide a means to mitigate individual

biases that are counterproductive (at odds with team or organizational culture).

To paraphrase John Donne, no team member is an island but rather “part of the main.”
