Assumptions Archive

Challenging assumptions exercises train our minds to become better problem solvers.

Assumptions often box us in. Challenging assumptions exercises free us.

Assumptions box us in. We like them though because they make our communications simpler. For instance, I am writing assuming you can read English. Assumptions can be conscious or unconscious. Either way, they anchor thoughts in our minds. They become the sides of the box outside of which we are trying to think. Challenging assumption exercises help to free us.

As I wrote previously, becoming better at challenging assumptions is about practice. Here, I offer three challenging assumptions exercises as practice. They will help us train our minds to see hidden assumptions.

Exercise #1: Hidden Sales Assumption

Do you want to buy the red one or blue one?

Salespeople frequently use these types of questions in various forms. The unsaid assumption is:

You want to buy either one.

Exercise #2: Leading Assumption

Many times descriptors influence how we think. For example, consider these two statements:

  1. This plan will get it done because it worked in a similar situation.
  2. This good plan will get it done because it worked in a similar situation.

Both give evidence why the plan will get it done. In the second one though, “good” is added. The assumption now is:

This is a good plan.

The statement does not define what good means. It is a leading assumption because it leads us to believe it is good because it will get the job done. We might need to consider other factors such as cost before we determine whether the plan is a good one.

Exercise #3: Hidden Leading Assumption

This occurs when we use words or phrases with positive connotations to lock in a view.

Everything went according to plan.

We tend to feel good when things go according to plan. It does not account for the fact that perhaps we missed great opportunities not in the plan. Thus, if we ask, “How did everything go?” and people wanted to hide the fact that they ignored a great opportunity that came up, they could respond with, “Everything went according to plan,” leading us to think all went well.


These challenging assumptions exercises help train our minds. Assumptions are not bad. They make communication easier. We just need to prevent them from boxing us in.


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Dollar Sign & PeopleOnce upon a time, long, long ago, a successful retail executive confided that in college she didn’t do well in economics. She mused how that could be considering her successful career at discerning consumer tastes. I replied, “Well, that’s because you’re good at understanding how real people behave. Economics is about fake people. It didn’t make sense to you because deep down inside you knew people didn’t behave the way economics said they did.”

Supporting this comment, Brendan Greeley’s article, “Economists Discover the Poor Behave Differently From the Rich” (BloombergBusinessweek, November 7, 2013 Edition) tells of an economic conference in which economists finally concluded that “people are different.” When trying to interpret consumer behavior, economic models use a “’representative agent,’ a single imaginary person who stands for everyone.” That means it doesn’t matter whether it’s your dad, mom, spouse, child, friend, enemy, boss or co-worker, the models assume same behavior from each. Worse yet, they all assume they behave rationally and plan for their future. Sound like all your connections? Wonder why these models poorly predicted outcomes during and after the financial crisis?

In defense of economics, behavioral economics is trying to rectify some of this by incorporating more psychology. Others, like Per Krusell of Stockholm University, are developing and working with “multiple-agent models” that will allow assumptions of people being different.

But, let’s return to our retail executive. Again, perhaps economics didn’t make sense to her because it’s not sensible? Still, how many times do we make decisions assuming all people, all consumers will behave similarly? Are we as guilty? I guess economists are just human too, and humans are frequently illogical and irrational. So, doesn’t it make sense that their models should be too?

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Sand and Water in GlassIf you don’t like the rules, change them. The article, “Boundary Problems” [The Economist, August 3, 2013 edition], reports that the United States “has changed the way it measures GDP.” More importantly, the article summarizes the history and depth of this statistic’s arbitrary nature. The questions it poses serve as a problem-solving primer on attacking any business statistics to uncover hidden problems and solutions.

When we write down a problem to solve, attacking definitions is a great idea. Attacking the origins of statistics is essentially the same thing when we attack the assumptions defining them such as what, how, when and where to count. Their arbitrary birth comes from the fact that someone has to make these assumptions. Once we shake the foundations of these assumptions, we take on a different perspective, thus creating problem-solving opportunities.

This happens because just as no set of sporting statistics can capture the reality of a sporting event, no set of business statistics can do the same for a business. Statistics perform their quantifying job by ignoring parts of reality. Within these statistical voids defining our problem are solutions. If you fill a 12-ounce glass with fine, playground sand, even though it looks full, you can add about 4-5 ounces of water. This serves as a metaphor for the illusionary fullness statistics often present us and for the hidden solutions in their gaps.

To uncover these gaps, we ask others and ourselves questions such as, why are we counting:

  • This?
  • In this way?
  • At that time?
  • At that place?

Then, we ask, What tangibles and intangibles are missing?

Since numbers naturally attract us, we tend to focus here. So, let’s ask, What’s the most vital immeasurable of our businesses? We just found a statistical gap filled with solutions.


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Combined 01Look at the figure to the right. The top is a multicolored square, the bottom a gray one. Yet, only one single attribute distinguishes the two: the top is a 10,000-x magnification of the bottom. The “gray” square is too large for me to load on the blog; however, if you copy and magnify it, you can begin to make out the multicolored squares although condensing added grayness.

Why is this interesting? Well, it shows how our minds work to help us . . . and delude us. Our physical attributes can teach us about our non-physical ones. For instance, if everyone is physically unique, then we can reasonable conclude everyone has unique personalities too.

These diagrams taught me two things. First, even though the individual squares are too small for my eyes, my eyes must put something in that space. Similar to the blind spot our eyes fill, my eyes automatically do this. Second, my eyes blur individual distinctions to fill the void with gray, thus simplifying things for me.

Our minds do the same with mental complexities such as people. For instance, upon entering a room filled with three hundred people, we work to fill our knowledge void by asking something like, “What group is this?” or “Who are these people?” Thus, mentally we process individual complexities (multicolored squares) into a simplified group (one gray square).

Grouping speeds our assimilation of knowledge at the expense of depth. Grouping comforts us with the illusion that we know something. It quickly fills our knowledge void similar to the way fast food fills our hungry stomachs with empty calories. Therefore, in business, grouping creates targets of opportunities if we are motivated to dive into the details, especially with talent. Grouping is natural, but no two people are the same.


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Definitions = Castle

Stories form boxes outside of which we try to think.

Stories galvanize people, helping them to learn, to coalesce around ideas. If we look at this galvanization as solidification, we can also see how stories could thwart change and innovation.

In a book review titled “Dead Certain” [The New Yorker, November 29, 2010 edition], I came across this passage by George Packer quoting Dan McAdams, a psychology professor at Northwestern University:

Psychological research shows that powerful narratives in people’s lives make it nearly impossible, in many cases, to consider ideas, opinions, possibilities, and facts that run counter to the story

Now, let’s expand this thought. Stories have many aspects and many forms: allegories, rationales, themes, descriptions, characterizations, histories, records, chronicles, accounts, testimonies, anecdotes, biographies, depictions, portraits, assumptions and statements. All can create vivid pictures of events, ideas, plans and strategies.

These pictures define facts, accepted truths, and work the same as definitions. Stories become the box outside of which we are trying to think. They come to define the culture, traditions, practices and expectations of our businesses.

For instance, a business whose history, pride and success come from a particular market will find difficulty deemphasizing it as it diminishes. It will also find it hard to seize opportunities that it has traditionally scoffed.

Business plans are other examples of stories’ influence. Plans are not only stories themselves, but they contain smaller stories such as rationales, accounts, descriptions and assumptions. For instance, the main assumption driving plans and strategies is that last year is a great baseline for projecting this year, that this year won’t be vastly different from last year.

Therefore, when we seek to change, to innovate, we will likely need to question the validity of existing stories no matter how factual and truthful they seem. They are likely prisons inhibiting us from considering what is outside their walls.


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KISS Principle Keeps Us Simply StupidLifting light weights with little effort doesn’t develop our muscles; lifting heavy weights with difficulty does. However, for some reason we believe the easier it is to absorb an idea the better the teaching is (i.e. KISS Principle – Keep It Simple Stupid). Knowledge isn’t power though; thought is. Our thinking processes are like our muscular ones: difficulty encourages their growth. They develop better when problems force us to think rather than to have us just through the motions.

Ian Leslie, in his article, “The Uses of Difficulty” (Intelligent Life, November/December 2012 edition), reinforces the importance of difficulty when he says:

In schools, teachers and pupils alike often assume that if a concept has been easy to learn, then the lesson has been successful. But numerous studies have now found that when classroom material is made harder to absorb, pupils retain more of it over the long term, and understand it on a deeper level.

We can also connect difficulty to a learning premise: people learn better by doing, not listening or watching. Thinking is doing for the mind. This is the basis of the Socratic Method; it encourages critical thinking. It makes our mind work.

Yet, how often do we hear:

  • I understand why you’re asking these questions, but can we just get to the point?
  • Let’s speed this up. Just tell me what I have to know.
  • Can you just send me your summary and conclusions?

As a result, people expect to get a healthy mind in the same way they expect to get a healthy body: without any effort or difficulty. The problem is that while our trainers tell us this is unrealistic for our bodies, the business world encourages it for our minds.


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Hell Image Text [IMG-0124]Businesses strive for predictability. Standardization helps them achieve that. Still, many employees like their jobs for their variability, “It’s something different every day.” Herein is a paradox.

On one hand, we have predictability containing expenses by minimizing surprises. On the other hand, work’s variability gives us pleasure. Could predictability make us wealthy but miserable too? Walter Kirn touches on this paradox in his article “Knowledge of the Future Is Messing With the Present” (The Atlantic, July/August edition) by asking:

Has making life more explicable actually made it any more pleasurable?

Perhaps by understanding predictability better, we could appreciate change better and strip its fearsomeness. The The Twilight Zone episode, “A Nice Place to Visit,” can help.

The main character, Rocky, is a petty thief who dies. A divine guide finds him to deliver the news and show him to his new “home.” At first, Rocky can’t believe his luck for in this place he gets whatever he wants. In poker, all the cards go his way. With women, none deny him. Despite his long list of sins, Rocky figures God granted him heaven.

However, after a while, he becomes bored with the predictability of succeeding at whatever he attempts, poker, slots, women, robberies, billiards etc. Finally, he approaches his divine host and says, “If I gotta stay here another day, I’m gonna go nuts! Look, look, I don’t belong in Heaven, see? I want to go to the other place.”

The divinity rebuts, “Heaven? Whatever gave you the idea that you were in heaven, Mr. Valentine? This IS the other place!”

By imagining extremes, we alter our perspectives, permitting a more realistic assessment of our conditions. Not only do these perspectives influence our emotions (i.e. reducing fear of change) but also they improve our problem-solving skills.



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Early on, I learned about GIGO, Garbage In Garbage Out. Translated, if you don’t input good data into the computer don’t expect good data in return. Even though a common assumption, as computers become more sophisticated so has their GIGO. They not only receive data from us but also from sensors programmed by us. Consequently, algorithms that are more sophisticated manipulate this data, making it increasingly hard to see inaccuracies and to believe it could be inaccurate.

The article, “Burning Question” (The Atlantic, September 2012 edition), by Michael Behar exemplifies this by exploring the problem long-standing computer models had in predicting the wildfires of 2012. Much of it had to do with the computer models operating on historical experience, thus assuming fires would behave pretty much as they did in the past. As a result, the article delivers this point, which really applies to all of us when we enlist the help of computers:

. . . some experts worry that younger fire analysts lean a bit too heavily on their data-crunching skills, and have little field experience. Dawson is thankful to have spent his early career fighting fires with an ax and a shovel.

This challenges us further because even if we suspect the data, they can still influence us. They anchor in our minds and we unconsciously judge other data against them, even our own observations. Thus, not only does the data taint our observations, but the computer’s sophistication could lead us to doubt what we saw. It’s very similar to people giving false confessions because they believe the police know what they are doing, except here we believe the computer “knows what it’s doing.”

So remember, if you want your computers to work better, remember to apply your experience. After all, they don’t have any.


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This entry is part 13 of 15 in the series Creative Innovation

One of the points Giovanni Gavetti makes in “The New Psychology of Strategic Leadership” (Harvard Business Review, July-August 2011 edition) about associative thinking but holds true for all aspects of creative innovation and decision making are our own biases. As a result of “the human mind’s confirmatory nature,” “Strategists often look selectively for evidence that supports the analogy” they’ve formed in associative thinking.

In other words, when doing our research we are more inclined to focus on evidence, or types of evidence, supporting our points rather than contradicting them. For instance, we might value statistical evidence over anecdotal or empirical evidence. We might value evidence produced by the scientific method rather than an alternative process such as trial and err. Yet, in both cases, accepting different types of evidence or evidence produced by different processes, stimulates creativity. Moreover, by holding the team to these things, such as requiring quantification, not only do we restrict creativity but we reinforce the status quo, inertia.

However, it’s difficult for people to come out from under their own biases. This means it becomes incumbent for the managers of these teams to be prepared and have the talent to lead the change that innovation brings. One thing that truly distinguishes leadership from management is the degree to which each must promote change. That includes change in evidence and processes the team will consider in evaluating options.

Thus, while diversity in our creative innovation teams is important, diversity in our approaches and processes to tackle problems and to make decisions are too. We can look at an organization’s policies and processes as a form of “group bias” that can impose itself on our teams and drastically negate their inherent advantages.

Beware of not only individual biases but institutional ones too.

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Identifying creativity isn’t easy, but it is possible and can be done without assessment tools. It begins with identifying outlying answers to our questions. There are differences between people who give standard answers and creative ones, and differences between people who can solve problems and people who are problem solvers.

In other words, when we ask people questions, we should to anticipate their answers. The more different they are from the ones we expect the more creative they might be. Of course, different is necessarily creative. For example, someone gives a different answer to a question, but when we ask, “How did you come to do that?” and they answer along the lines of, “Well, some friends suggested I do that,” the act might be different to us but was not creative to the person.

This technique is similar to polling, meaning we need several questions on divergent topics to use it well. We also need to adjust for cultural and environmental differences. For example, people might give us different answers because their culture is different from ours and we have little experience with theirs. Thus, while their answers might be different from what we expect, our expectations might be too narrow.

On the other hand, their answers can’t be unconnected to our question. For example, if we ask, “How do you normally get to work?” and they answer, “Breakfast,” while it’s different, there isn’t a connection, and we’ll need to ask for one.

Nevertheless, in the end, the more different we find people’s answers are from our expectations and from what we could expect from their situations, the greater their creative potential is. That brings up another point: most people don’t realize how creative they are so they haven’t developed it.


Related article: Test Your Creativity: 5 Classic Creative Challenges


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Emotions in Decision-makingImagine preparing to board a cruise ship, and a Quantnik stops you.

He asks, “What is your suitcase’s volume?”

You are puzzled and slowly say, “What? . . . I don’t know.”

“How about the ship?” he continues, “Do you know its volume?”

Again, puzzled, you respond tentatively, “Noooo.”

“Well, I’m sorry, but you cannot board the ship.”

Now, your puzzled stupor vanishes because you’ve anticipated this trip for some time. Thus, with raised voice, you now become the interrogator, “Why not!?”

With an insulting calmness, the Quantnik answers, “Because we don’t know if your luggage will fit on the ship.”

This irritates you because you feel any idiot can see that the luggage will fit. Yet, I find that it’s not uncommon in business to find stalled decisions because someone could not measure the desired outcome against the required inputs when the answer was intuitively simple. For example, in one case, there were persistent errors in shipping, causing upset customers. Yet, before establishing a process to fix it, several managers wanted to research the return we would get from the new process against our expected costs to establish the process. To this, the sales manager sarcastically responded, “By the time we research that, we won’t have any customers left.”

What is important to remember is that humans have a natural attraction to numbers; yet, this attraction can become an illness just as our attraction to fats and sugars can be (obesity). This doesn’t even include the many assumptions we often need to apply to arrive at the numbers want. Therefore, just because numbers exist doesn’t mean we are any less subjective in our decision making than if numbers didn’t exist.

So, what’s a Quantnik? It’s similar to an alcoholic but the addiction is to numbers, and thus more quantifiable.



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Often we assume fast service is good service; however, we need to challenge constantly this assumption. As I wrote previously, customer service borders on entertainment. To help people feel this, I use my Red Ferrari Analogy. I introduce it by asking, “When you go home from work, which way do you take?” Eventually, people agree: the quickest.

From here, I continue with the following:

All right then, let’s say that instead of going home in your present car at the end of today I give you the keys to a red Ferrari to drive home. How would you like that?

People agree that they would like this very much; however, I then present a caveat:

Great, however, there is only one condition: once you reach home, you give the keys and the car back to me. Now, let me ask you this: Knowing this would you still take the quickest way home?

At this point, people are usually laughing but say, “No.” I’ve even had a few who said, “I would never go home.” Nevertheless, I finish with this point:

So, the question is this: Are you providing people with a quick experience or a Ferrari experience?


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How we position things greatly influences the outcome. In the April 7, 2012 edition of The Economist the article, “Dressing Up,” uncovers that women’s sizes have inflated by four sizes since the 1970’s. Unlike men’s sizing which is based on inches, women’s sizing is purely arbitrary and often varies by brand. Thus, depending on the size, a pair of women’s pants might have increased as much as four inches at the waist and three inches at the hips since then.

The generally accepted assumption for allowing this size inflation is that if consumers feel good about themselves they are likely to buy, thus why the fashion industry calls it “vanity sizing.” However, even though it seems like a topic to take lightly or with which to have fun, vanity sizing plays in all aspects of statistics. That is why it’s important to challenge definitions and assumptions in order to understand and solve problems.

For instance, the article “Botox and Beancounting” of the The Economist’s April 27, 2011 edition, discusses how official U.S. economic statistics might be overinflating its performance relative to Western European economies. Ironically, the article’s title makes an appropriate analogy to vanity sizing.

U.S. unemployment figures present another excellent example. They not only conflict with one another on occasions but they are difficult to figure. Additionally, their accounting changed in the 1980’s, making them appear lower than before.

Thus, while it’s commonly said that “numbers don’t lie,” that’s true; however, an ignoramus isn’t lying either if he believes his own ignorance. If we’re ignorant to numbers’ origination, we are more likely to accept them if they tell us our glass is half full rather than half empty, thus reinforcing our own perceptions . . . also known as “vanity believing.”


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House of Arbitrariness & Conditionality


We often view measurements as unchangeable. A meter is a meter, a pound a pound. We often forget that at some time someone somewhere declared what those were and that they would be a standard. The point is this: arbitrariness underlies almost all objective standards by which we live.

For example, in the January 29, 2011 edition of The Economist, the article, “The Constant Gardeners”, explores the kilogram. The official standard is a platinum-iridium alloy cast in 1879. However, today, its weight seems to vary from its copies by up to 69 micrograms, about half a grain of sand, an important variance when weighing small things. So, the question is this: How heavy is a kilogram . . . really?

The relevancy to problem solving is similar to that which I wrote in my post, “Arbitrariness: The Cornerstone of Conditions”:

By searching for the underlying arbitrary aspect of any apparently objective situation, we can often find the perspective – when altered – that can cause us to see that situation in a different light.

For example, when someone asks us, “What’s the best way to get from A to B?” we often give the fastest route. The assumption being that the “best way” is “fastest” when “best” could have many different attributes. Over time, the best-fastest link becomes the arbitrary point – when altered – that sheds a different light on what route might be best such as the most scenic one or the most fuel-efficient.

As a more sophisticated example, consider our reliance upon “proven outcomes.” What does that mean especially when you cannot scientifically prove that good leadership begets good results? Thus, when we look at what it took to be proven, we often find that it’s subjective based upon who is determining what “good leadership” and “good results” are.


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Even though writing down the problem can help us solve it, it’s also a form of defining the problem. Thus, we will tend to define problems according to a nomenclature that we typically use. Since problems don’t care how we define them, our problem-solving approach problem will tend to be clunky and segregated rather than smooth and integrated.

For example, below is a schematic. On the left is a typical functional perspective of business. On the right how a problem has no regard for those functional boundaries.


While obvious, we easily forget. For instance, if we define a problem as, “We need to generate more sales,” we will automatically tend to view it initially as a Sales & Marketing problem. In actuality though, many aspects such as pricing, delivery, servicing, management and technology could exist.

Therefore, in solving problems, it’s best that we assume the solution is an integrated rather than a segregated one. In other words, rather than ask something such as:

  • Is this part of the problem?
  • Does the problem affect this?

We should ask whether we can prove without a doubt that:

  • This isn’t a part of the problem?
  • The problem doesn’t affect this?

Thus, returning to the above example, rather than start from the premise that it’s a sales and marketing problem and then see if any other area is affected, start from the assumption it’s a business-wide, integrated problem and eliminate areas as we conclusively prove that they aren’t involved.

By assuming the problem is bigger and more integrated than we initially perceive it, we expand our field of potential solutions and success. Moreover, since we aren’t omniscient, it’s often better to assume the problem is more involved than it initially seems.


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