Why do we focus on trivial things?


, explained.

What is Bikeshedding?

Bikeshedding, also known as Parkinson’s law of triviality, describes our tendency to devote a disproportionate amount of our time to menial and trivial matters while leaving important matters unattended.

Where this bias occurs

Do you ever remember sitting in class and having a teacher get off track from a lesson plan? They may have spent a large portion of your biology lecture time telling you a personal story or skimmed over an important scientific theory. In such an instance, your teacher may have fallen victim to bikeshedding, where they spent too long discussing something minor and lost sight of what was really important. Even though it may have been more entertaining to listen to their story, it did not help you acquire the necessary facts for your exam next week.

Bikeshedding is also a common occurrence in corporate and consulting environments, especially during meetings. Imagine you have a meeting scheduled with your colleagues to discuss two important issues. The first issue is having to come up with ways in which the company can reduce carbon emissions. The second issue is discussing the implementation of standing desks at the office. It is clear that the first issue is more important, but also more complex. You and your coworkers will likely find it much easier to talk about whether or not to get standing desks. As a result, everyone devotes a large portion of the scheduled meeting time to this more trivial matter. 

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Individual effects

Bikeshedding can negatively impact personal productivity because it causes us to manage our time inefficiently by disproportionately allocating time to tasks. We end up spending too long on trivial tasks and leave ourselves no time to complete the more complex tasks, which tend to be more important in the grand scheme of things. Bikeshedding causes us to be short-sighted with our time allocation, going with the most straightforward task first because we believe it will take less time to complete.

For example, if our to-do list for the day includes going to the grocery store, folding the laundry, and submitting our tax forms, we may spend more time getting groceries and folding laundry because these are easy, menial tasks. By the time we get around to submitting the tax forms, we barely have enough time left. Thanks to bikeshedding, we have put off the most important task and wasted our time on things that are easy to check off our to-do list.

Systemic effects

Bikeshedding is most dangerous in group settings because each individual devotes more time to simple tasks, causing the collective time spent on trivial matters to snowball.

Our tendency to focus on trivial issues makes companies operate at a suboptimal level because they forget to allocate their time efficiently. This causes important proposals to take much longer than necessary to come to a head, as they are left unattended for too long.

Bikeshedding can also cause the final product of a project to suffer because the team has spent most of its time working on small, simple components instead of the important, complex parts. For example, when designing a flier, a team may spend a long time picking out fonts and colors, leaving them with less time to decide what the text should actually say.

How it affects product

We might succumb to bikeshedding while designing digital products. Imagine a team is in the midst of developing an innovative navigation app. Rather than dedicating their resources to refining their groundbreaking traffic tracker or adjusting the interface to help drivers better maneuver alternate routes, the designers get caught up choosing the exact shade of blue to distinguish bodies of water. As a result, the app looks great, but isn’t nearly as innovative as promised.

Even if you aren’t an app designer, bikeshedding may skew your priorities when purchasing. You might devote your time to determining which color you want your new phone to be, and neglect bigger elements like how many gigabytes it has. Only later do you realize your mistake, after quickly running out of storage.

Whether we’re on the side of production or consumption, bikeshedding is decreasing the quality of our goods. With this in mind, we should rank our priorities before approaching a new product to make sure we stick to them throughout.

Bikeshedding and AI

Bikeshedding may impact how we approach building new machine learning models. AI practitioners may become engrossed in fine-tuning model hyperparameters: the values dictating how an algorithm interprets a dataset. For instance, if a real estate startup is designing an AI tool for predicting housing costs in a rapidly evolving urban area, the practitioners might spend all their time deciding the exact perimeter from which they will extract their data from.

However, when too consumed by hyperparameters, practitioners might forget to pay attention to the quality of the data itself. This means that machine learning will learn to generate outputs based on inaccurate information, making it impossible to generalize its results to the real world. In our real estate example, the design team might neglect reviewing when the original housing data was collected, allowing outdated data points to make their way into the mix. Once the practitioners are finally finished training the algorithm, they unfortunately discover that its predictions are way off from the actual recent costs of living in that community.

Why it happens

Bikeshedding occurs because trivial tasks are easier to comprehend than more complex issues. We feel more comfortable working on and discussing simpler issues.

In corporate settings, we often have the opportunity to voice our opinions. It is much easier for us to spend time discussing a topic that we already understand and that others do, too. We feel more competent taking a stance on the issues we are well-versed in and snatch the opportunity to say something.1 In turn, team members also want to contribute to show that they are actively listening, causing too much time to be spent discussing a trivial matter. These opinions often don’t actually add much value to the discussion and cause us to waste our time. By attempting to show off our knowledge, we shy away from addressing new and probably more complex issues that are of greater importance.

Another reason behind bikeshedding may be that we believe that the people putting forward a complex issue have a better understanding of it than we do.2 We don’t want to take on responsibility for such a difficult matter and end up relying on the idea that someone else must already have spent time looking into it.

Why it is important

It is crucial to be aware of bikeshedding because it helps identify instances in which one of our most valuable resources—time—is being wasted on trivial matters. Bikeshedding means that we are failing to operate efficiently and may not complete everything that we have set out to.

As we have discussed, bikeshedding has implications in both our personal and group settings. Because of its ubiquity in our lives, it is vital that we try to counter the effects of this bias. On an individual level, it affects our to-do lists and keeps us from meeting our goals. It can also influence our productivity because we easily get off track by fixating on the easy components of an assigned task. This may actually cause the final product to fall short of the mark.

For example, imagine that you are asked to write an article about Einstein’s theory of relativity. This concept is very difficult to grasp, so you spend a disproportionate amount of space in the article discussing Einstein’s personal life, and only a few lines actually getting to the scientific breakthrough. Bikeshedding has caused not only your productivity to suffer, but the finished article as well, as it focuses on the miscellaneous details that are easy to understand and may not enhance your reader’s knowledge of the scientific concept.

Bikeshedding may have even more serious implications on a group level because once an opinion is voiced on a simple issue, more and more people jump in to give their own take. Time is not being managed effectively, and important issues that a corporation needs to tackle end up only having a few minutes of people’s attention.

How to avoid it

Awareness of bikeshedding is vital to countering its effects. There are various techniques that we can use to ensure that a group is being efficient with the time they spend on each topic.

One method to avoid bikeshedding is to have a separate meeting for focusing specifically on any major, complex issue. If the topic is brought up in a meeting with a long agenda, it becomes overshadowed by trivial issues. However, if it is made the sole purpose for a meeting, it is difficult to avoid talking about it. Keeping meetings specific and focused on a particular issue can help counter bikeshedding.1 It may also be a good idea to have a particular person appointed to keep the team on task and pull back focus if the discussion happens to get sidetracked.

Another way of pulling the focus onto a particular issue is to have less people present at a meeting. Bikeshedding is a big problem in group settings because simple issues entice multiple people to speak, which can drag them out. By only including the necessary number of people, trivial issues will take up less time, even if that happens to come up.

How it all started

The term bikeshedding comes from Cyril Northcote Parkinson, a British naval historian most famous for Parkinson’s law, which posits that work expands to fill the time allocated to it. For instance, if you allocate an hour to a task that actually only takes 30 minutes, the task will still end up acquiring the complexity of an hour-long task.3

After putting forward Parkinson’s law in 1955, Parkinson discovered a lesser-known phenomenon called the law of triviality, describing how organizations tend to focus on trivial issues and put aside more complex matters.

In particular, Parkinson’s law of triviality states that the amount of time spent discussing an issue in an organization is inversely proportional to its actual importance in the grand scheme of things. In other words, the less important an issue is, the more time is spent on it.1

Parkinson outlined the law of triviality through a metaphorical story.1 He asked people to imagine a financial committee meeting where there were three matters on the agenda:

  1. A proposal for a £10 million nuclear plant
  2. A proposal for a £350 bike shed
  3. A proposal for a £21 annual coffee budget

He suggested that the committee would look past the first proposal because it is too difficult for people to voice their opinion on such a complicated issue. The committee would quickly move on to the proposal for the bike shed and spend far more time discussing it than they did the nuclear plant. Finally, they would spend the most amount of time discussing the coffee budget, as the simplest of the three proposals.1

Due to this example, Parkinson’s law of triviality became known as bikeshedding, which is the term more commonly used today.

Example 1 – Bikeshedding and data science

Bikeshedding can also occur when a team is faced with an overwhelming amount of data. Large data sets can be hard to understand, leading us to try and summarize them in a more digestible format. While it may be important to encapsulate large data sets so that more people can grasp the main take-aways, bikeshedding shifts our focus to menial components and away from the bigger picture.

Reid Holmes, a professor of computer science, describes this scenario in software engineering, where scientists get bogged down on simple issues when summarizing large data sets.4 They may spend too much time deciding on which program to use, what the column names should be, and then how to format those columns. During all that time, the data is just sitting there, left unaddressed. It is not being made a priority because it is overwhelming, so the scientists fixate on simple decisions instead.

Even the decision to present data in columns, known as tabular summarization, may be a consequence of bikeshedding. This may seem like the most intuitive way to organize the data. However, grouping together large amounts of data into only a few columns can cause us to lose sight of relationships between discrete data points.4

In short, bikeshedding causes software engineers to spend too much time formatting tasks. In addition, the instinct to use a simple method of summarization might make us miss out on causation expressed by data.4

Example 2 – Zoom as a solution to bikeshedding

Months into the COVID-19 pandemic, Zoom meetings became the new normal. We began discussing important issues online instead of in boardrooms. While we may have missed in-person interaction, Zoom may have actually helped us avoid bikeshedding.5

The basic Zoom package allows people to have 45-minute meetings for free. Harvey Schachter, a writer specializing in management, suggests that this design is a perfect antidote for bikeshedding because it is a built-in time management tool.5 Knowing that our team only has 45 minutes to conduct a meeting helps us stay focused on the major points of discussion. Zoom even gives us reminders of how much time is left, meaning if the discussion has gotten off track, these reminders may help pull the group back to the important issue. Zoom takes the place of an in-person timekeeper and may help ensure we fulfill the purpose of the meeting because going overtime is simply not an option.

Moreover, Zoom may help reduce bikeshedding by only allowing one item on a meeting agenda. Parkinson’s law states that issues will end up expanding to the time allocated to them, meaning that if we devote an entire 45-minute meeting to an issue, we are likely to use the entire Zoom call discussing that issue. This can be useful for complex ideas that require lengthy discussions.


What it is

Bikeshedding describes our tendency to spend too much time discussing trivial matters, and too little time discussing important matters. This bias denotes an inverse relationship between time spent and the importance of an issue.

Why it happens

Bikeshedding occurs because it is much easier to discuss simple issues we are confident that we comprehend. In group settings, we often look to voice our opinions as a sign of participation. We are more likely to talk about a relatively simple issue because it is daunting to discuss a complicated issue, even if it is more important.

Example 1 – Bikeshedding and large data sets

Large data sets can be overwhelming to tackle. As a result, scientists may spend too much time discussing simple matters like which program to use, and not enough time actually analyzing the data. Another effect of bikeshedding is our tendency to choose the simplest method for summarizing data. . Grouping discrete data points can cause us to overlook interesting relationships between data.

Example 2 – Zoom: the antidote to bikeshedding

Zoom has become very popular as we have transitioned to working from home during the COVID-19 pandemic. The free version of Zoom only allows a 45-minute meeting. This set time limit ensures that we’ll devote our focus to the important issues and resist wasting too much discussion on trivial matters.

How to avoid it

Bikeshedding can be avoided by attempting to remain on topic. To stay focused on important issues, we can implement single agenda-item meetings which makes us less likely to get off track. In addition, we can assign a specific person to ensure that we do not spend too much time on unimportant issues. A final way to limit bikeshedding is to have fewer people attend a meeting, as that way there will be less people to voice their opinion on trivial matters.

Related TDL articles

The Eisenhower Matrix

Bikeshedding makes it difficult for us to keep our priorities in line. We fixate on trivial matters, without remembering to address the bigger picture. A scientifically proven method for getting back on track is the Eisenhower Matrix, a time-management strategy that helps us determine which tasks should be prioritized, which can be delegated, and which can be tackled at a different time. Read this article to learn how to better organize your time.


Believe it or not, bikeshedding is a form of procrastination. Rather than tackling the hard stuff first, we push it to the side and preoccupy ourselves with the easy stuff instead. This coping mechanism drains our time and energy, leaving us in a rush to get done what actually matters. Read this article to learn about other forms of procrastination and how to avoid them using cognitive science.


  1. Farnam Street. (2020, April 17). Why we focus on trivial things. https://fs.blog/2020/04/bikeshed-effect/
  2. Effectiviology. (n.d.). Bikeshedding and the law of triviality: Why people focus on minor issues. Retrieved September 1, 2020, from https://effectiviology.com/bikeshedding-law-of-triviality/
  3. Falconer, J. (2017, November 14). How to use Parkinson’s law to your advantage. Lifehack. https://www.lifehack.org/articles/featured/how-to-use-parkinsons-law-to-your-advantage.html
  4. Holmes, R., & Zimmerman, T. (2016). Look for state transitions in temporal data. In T. Menzies & L. Williams (Eds.), Perspectives on Data Science for Software Engineering (pp. 133-135). Elsevier.
  5. Schachter, H. (2020, July 18). Explaining ‘bikeshedding’: When trivial things waste meeting time: Bikeshedding, or the law of triviality, can often eat up precious minutes in meetings as attendees get caught up with trivial topics. The Globe and Mail.

About the Authors

Dan Pilat's portrait

Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Sekoul Krastev's portrait

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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