what is a hypothesis tree

Hypotheses trees

Problem-solving

July 27, 2022

"The first principle is that you must not fool yourself, and you are the easiest person to fool." –Richard Feynman, Nobel Prize-winning physicist

This statement rings true for teams trying to solve complex problems. Business owners with experience in specific fields may think they know how to solve a problem—especially if they have encountered a similar issue in the past.

Leaders will usually form a hypothesis around a core problem and use it as a foundation to solve it. This can either speed up the process and solve the problem in a single meeting or lead to confirmation bias. If the latter happens, the wrong issues will be targeted and the problem won't be solved correctly.

This piece will look at the power—and the pitfalls—of using hypothesis trees to solve problems.

The problem's primary hypothesis could focus on reducing the costs of tunnel boring and moving traffic underground. However, that hypothesis also represents a very narrow view. So, another hypothesis could be investing in self-driving vehicles above-ground and public transportation, or encouraging businesses to let their employees work from home to reduce traffic.

Teams must also consider limiting team alignment. Since the hypothesis is forced early on, it doesn't let the data speak. One hypothesis must be picked, and it limits opinions or data on other hypotheses being discussed and can impact the team's ability to solve the problem successfully.

Finally, hypothesis trees are prone to confirmation bias. Humans have over 180 cognitive biases that our brains use to guide decision-making. But psychology experts say these biases are bad for business, as they can lead to incorrect decision-making, and teams can default to embracing group think. Hypothesis trees start with the strongest solution in our brain, and we are wired to believe it's correct because of our experience and expertise—which can be a disaster for business.

Rob Jenks is the senior vice president of Corporate Strategy at Tanium , a cybersecurity and systems management company.

His role has no room for bias, and every decision he makes has to be rooted in data and evidence.

According to Jenks, to overcome a structural problem it's best to break it down into parts and find bite-sized chunks that can be solved. When you piece it back together, the chunks will either prove (or disprove) the hypothesis you've been working on.

Jenks says it's important to remember a hypothesis is a provisional answer and a theory that needs to be investigated.

"The reason it works so well is that it makes the solution transparent and when you identify that last leaf on the hypothesis tree, you realize the subpart of the problem often is trivial," he says.

"I remember from my McKinsey days. We would always ask teams literally on the first day— what's the answer to this problem? What's your day one answer? What you're really saying is: what's your hypothesis? "

Even when a hypothesis is identified, Jenks says the pros and cons of using hypothesis trees to solve problems can't be forgotten.

The downside to using hypothesis trees, Jenks says, is you simply could be wrong.

"We might end up going down a path that's driven by the hypothesis that ends up being completely wrong," he says. "You can waste a lot of time as hypothesis-driven problem-solving is sometimes just confirmation bias."

Hypothesis trees can be a very effective and rapid method of solving problems.

"When you have an answer in mind, It's much easier (and more concrete) to then think about it and how to prove the hypothesis," Jenks says. "It dramatically accelerates the top-down structuring of problems, and it focuses on the acceleration to the answer and it cuts off extraneous work."

Use hypothesis trees wisely to speed up your problem-solving

Hypothesis trees are a powerful tool to speed up problem-solving—if they're used correctly.

Hypotheses are born out of expertise and experience. Backed up with evidence and data, they give businesses a head start when solving problems and can significantly speed up the process. There's a reason firms like McKinsey use them in daily interactions with their clients— they work .

However, there's a downfall to relying on hypotheses to solve problems—the human brain. Our brains are wired in cognitive bias. If we think we know the answer to a question or problem, we can forget that we need to back it up with facts. Missing this step when using hypothesis trees can lead to incorrect problem-solving and wasted time.

Our advice is to use hypothesis trees as they were designed: as a provisional answer to give your team a head-start to solve complex problems.

Now you know all about hypotheses trees—but have you heard about issue trees? Learn more.

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Crafting Cases

The Definitive Guide to Issue Trees

Introduction, issue trees: the secret to think like a mckinsey consultant and always have a clear, easy way to solve any problem.

Ask any McKinsey consultant what’s the #1 thing you should learn in order to solve problems like they do and you’re gonna get the same answer over and over again:

“You’ve gotta learn to create Issue Trees.”

Issue Trees (also known as “Logic Trees” and “Hypothesis Trees”) are THE most fundamental tool to structure and solve problems in a systematic way.

Mastering them is a requirement if you want to get a job in a top consulting firm, such as McKinsey, Bain and BCG.

But even if you’re not applying for a job at these firms, they’re a powerful tool for any job that requires you to solve problems .

In fact, Issue Trees are the main tool top management consultants use to solve the toughest multi-billion dollar problems their clients have.

This guide will teach you how to create and use Issue Trees.

I will give a focus on case interviews but you can use this skill in any other problem solving activity. I personally use it everyday at work.

(Which means what you’ll learn here is gonna be useful for far more than merely getting a job.)

About the author

I’m Bruno Nogueira.

I’m an ex-McKinsey consultant and I have learned to think using issue trees the hard way.

There were no good resources to learn this back when I was applying for the job.

Even within McKinsey there was no formal training. People just expected you to “get it” on the job.

After leaving the Firm, I’ve spent a few years coaching people to get a job in consulting, and I learned to teach this skill the only way possible: by actually teaching it!

Along with my partner Julio, I have taught 1000’s of people to break down problems in a structured way using issue trees.

And today I’m gonna teach you how to do this.

In this guide you'll learn:

Issue Tree Fundamentals

Three Techniques To Build Issue Trees

Six Principles For AMAZING Issue Trees

Issue Tree Examples

Common Mistakes and Questions

How To Practice Issue Trees

BONUS CHAPTER

Applying Issue Trees On The Job

Issue trees are the blueprint of how McKinsey (and other) consultants think.

They make your thinking process more rigorous and much, much more clear.

Unfortunately they didn’t teach you this well enough (if at all) in school.

They don’t even teach this in most Business Schools.

But if you learn to harness their power, you’re set to case interview success (and a career where every problem can be easily solved).

How I learned about Issue Trees

A bit of a personal story first…

I first learned about Issue Trees from a friend who was working in management consulting. It was back when I was applying for a job at McKinsey, Bain and BCG.

This friend told me Issue Trees were the #1 thing I had to learn in order to do well on the interview and land a top consulting job.

And so, the first thing I did was to look for examples of Issue Trees.

And I found stuff like this…

Not exactly rocket science, right?

But then I thought… “Alright, what if my problem is not a profit problem? Or what if I need to dig a little deeper than that?”

It didn’t take me long to find people on the internet telling me that you could use Issue Trees to solve any problem!

Here’s how they illustrated this important point:

Let’s be honest with ourselves here… This is NOT the best way to teach something!

And so I kept looking around.

I wanted to see realistic examples of real Issue Trees consultants use to solve their client’s problems.

And if I was lucky, I hoped to find some explanation on why each example was structured the way it was.

Here’s the kind of stuff I found looking up on Google again:

And now I was left wondering how to get from Point A (the simple profit Issue Tree from the beginning of this orange box) to Point B (the behemoth you see above).

And I also wondered if getting to this behemoth was actually the kind of thing I wanted in the first place. Would it help me in a real interview?

So I gave up on the internet and decided to learn Issue Trees from those who know it best: real consultants. That’s who I learned to build Issue Trees from.

But I know that most people don’t have access to real consultants with the time to teach them things.

And it never stopped bothering me the fact that the internet had no decent resource to teach people of a skill that I use multiple times a day (and even make a living out of).

This is why I wrote this guide.

The 4 things you need to "get" to understand Issue Trees

Before we jump into the nitty-gritty of how to create and use your Issue Trees, I want to give you a high-level view. This high-level view is what we’ll cover in this chapter.

I’m gonna show you four ways to look at Issue Trees so you can get an intuitive understanding of them.

And I’m gonna show you that through an example of a realistic Issue Tree.

They are a "map" of your problem

The first thing you need to know about Issue Trees is that they’re nothing more than a “map” of the problem.

Not just any map, but a clear and rigorous map.

We’re gonna achieve those two goals by using a principle called “MECE”. (Don’t worry about it now, we’re gonna get you covered later on).

So suppose you’re an executive in a Telecom Company in charge of B2C mobile services (that is, cell phone services for regular people like you and me).

Imagine you have a client retention problem. That means too many clients are unsubscribing for your services/plans.

How would you figure out what’s causing this problem?

Well, a smart executive would build a “map” of all the possible things that might be going on. This map is your Issue Tree and “the things that might be going on” are your hypotheses.

I’ll show you one of these, but before I do that, I will ask you to do one 15-second task:

**Action step: grab a piece of paper and make a list of all the hypotheses that pop-up into your head of why customers might be unsubscribing for this Telco’s mobile services.**

Now, take a look at this Issue Tree.

If I did my job right, every hypothesis you had fits one of the “buckets” in this tree.

How do I know that?

Well, I used the MECE principle I mentioned above to build this tree. This means every “part” of the problem is here and that each “part” is different/independent from each other.

We’re gonna get back to this later.

The second thing to notice is that there are probably whole categories of problems you didn’t even think of when you wrote out your list of hypotheses.

You’ve probably thought about customers hiring a competitor service because they hate us for a variety of reasons (unreliable service, poor customer service) and you’ve probably thought about them leaving us because they were lured by competition somehow (low prices, free phones).

And if you’re savvy on the telecom industry, you might have even though about customers moving to pre-paid services.

But if my intuition is good, you have probably forgotten about at least a couple of categories within the “They’re being forced out” branch.

For example, you might’ve forgotten to think that they may be cancelling subscriptions on purpose because they’re leaving a market.

Simple – I’ve done thousands of cases with hundreds of candidates to consulting jobs and most people forget about those.

The third thing to notice is that I didn’t even mention any specific hypotheses that you might have written on your piece of paper, things such as:

We’ve increased our prices and our competitors have dropped theirs
There were failures in our billing provider and a bunch of people were overcharged and got mad at us
Our network was down for several days due to a problem within our IT systems, leaving people offline
A problem in the banking system caused us not to receive several payments, which triggered subscriptions to be cancelled automatically

But still, all of these hypotheses (and thousands of others) would fit into one of the eight categories at the right-end of the Issue Tree.

All of this is to say that an Issue Tree is a map of the problem you have to solve.

Just like a good map it covers the whole problem area (you wouldn’t want a map of just a part of the territory you’re exploring).

And just like a good map, it doesn’t go into the slightest details (the specific hypotheses), but focuses on the broad aspects of your problem (the categories).

No adventurer should explore a territory without a good map.

And no smart problem solver should start solving a problem without a good Issue Tree.

Issue Trees are the tool for "dividing and conquering"

Issue Trees are more than a mere map. They’re a very useful one at that.

For those of you who are not warfare strategy geeks like me, “divide and conquer” is a military strategy based on attacking not the whole of the enemy’s forces at once, but instead, separating them and dealing with a part of their forces one at a time.

It’s much easier to deal with one cockroach a hundred times than with a hundred cockroaches at once (sorry for the nasty imagery for all cockroachofobics out there).

Anyway, this strategy goes back into the times of Sun Tzu (the ancient Chinese philosopher who wrote “Art of War”).

And it so happens that this “divide and conquer” strategy is not only good for dealing with military opponents, but also GREAT for dealing with Big, Hairy, Complex problems.

It’s very difficult to deal with a “customer retention problem” like our Telco Executive is facing right now without making this problem more specific first.

But if you try making it more specific without the help of an Issue Tree (or a “problem map”), you’re gonna end up with one of two things:

(1) An incomplete list of possible hypotheses (like the one you probably wrote down on your piece of paper)

(2) A HUGE list with hundreds, even thousands of hypotheses (which, at the end of the day, you don’t even know if it’s complete anyway)

Issue Trees allow you to divide the problem and work on it one part at a time.

Or, if you’re a Telco Executive like our friend from point #1, you can delegate this work to other people now that the problem is neatly divided.

Here’s an example of how you can divide the problem into tasks and delegate its parts:

On the left side are the 8 buckets at the end of our Issue Tree. These are the eight potential problem areas.

And in orange are the six tasks our executive must do to know what’s causing the problem.

Many of them are actually just requests to other people within the company because when you use “divide and conquer” you get to give work to other people (which by the way, it’s a great way to grow your career quickly).

Depending on what they find Task #1, you may be able to stop there. Or you may need to do all 6 tasks and then some more as you find new, unexpected information.

Now, I know that this Telco Executive doesn’t seem like a really good professional when I put the Issue Tree and the tasks that way. He doesn’t even know the basics about what’s going on in his company!

But let’s pretend for a second that he was just hired and he’s not at fault for not knowing his company’s basic numbers.

Or that he’s actually a management consultant instead of an executive, and that he was hired to give this company’s executives an unbiased perspective of why customers are leaving.

Now things make more sense!

But the point is that the Issue Tree allows you to create a plan to solve the problem, just like a map allows you to create a route to get from Point A to Point B.

Issue Trees are excellent for prioritization

Not only Issue Trees let you have a “map” of the problem and help you create a “route” on how to solve it, they also give you the ability to anticipate a lot of stuff that could happen along that route.

And anticipation = prioritization.

(Or 80/20, for those of you who love the buzzwords).

Because Issue Trees lay out the underlying structure of your problem, they help you with two things:

(1) Get data structured in a way that helps you quickly find out where the problem is

(2) Anticipate what happened with a moderate to high degree of confidence even before you get data.

Let’s tackle each of these individually.

(1) Issue trees help you get data structured in a way that’s helpful to prioritize the problem.

Suppose you’re the Telco Executive and you’ve built your Issue Tree.

Remember how his Task #1 was to ask the Business Intelligence unit of his company for hard data about what’s going on?

Let’s assume they came back with the data below – how would you prioritize the problem?

The way I see it:

Of the 6.5 thousand extra people who unsubscribed this year compared to last year, the vast majority came (4.5) from a system failure. This is not acceptable and this should be the area this executive should tackle first.

But there’s also another area that calls my attention: our biggest source of customer churn – them going to competitors – has increased from 7k per year to 10k per year.

This person (and the company) has two different problems, and getting data in a structured format via the Issue Tree makes this very clear.

(2) Issue Trees help you make a really good guess of what might be going on even before you get any data

Suppose this company’s Business Intelligence division is not that intelligent and has no data to provide.

In fact, suppose this company has such a problem with data gathering that they can’t get structured data for pretty much anything.

This would make this problem a nightmare to solve.

With no structured data, this exec (or his subordinates) would need to do a lot of legwork to test each category of hypotheses:

To know if customers are hiring a competitor service, we’d need to call a large sample of them and ask
To know if a problem in our processes caused customers’ subscriptions to be accidentally cancelled, we’d need to map out all our processes that could’ve caused that and evaluate each one individually

You get the idea!

But Issue Trees are a map of the problem. And as any good map, we can use it to see what parts of the terrain seem to be more important than others.

Here’s an example of how to do that even if you have no data:

Obviously you need to use logical reasoning and a bunch of assumptions to prioritize one of these categories as more likely than others.

But in the absence of data that’s actually the best way to work!

So if I were this executive and there was no data, I’d try to work smart and start testing the most likely hypotheses.

This means I’d give more priority to the ones related to customers leaving us willingly.

It customers were being forced out we’d have crazy call centers full of customer complaints and the executive would probably know about it already. We’d probably have some lawsuits already!

I won’t go into the weeds of how to prioritize as we already cover that in our courses (including our free 7-day course on case interview fundamentals) but for now it’s cool to know that Issue Trees are the tool that enables you to prioritize effectively because it gives you a clear map of the problem.

You can have "problem trees" and "solution trees"

Last thing about Issue Trees that you must know to grasp what they are even before we can go into the specifics on how to build them is that you can have “Problem Trees” and “Solution Trees”.

Or, as I like to call them, “Why Trees” and “How Trees” .

“Why Trees”, also known as “Hypothesis Trees” are the one we’ve been working with so far.

You have a PROBLEM and you want to know WHY it’s happening. Then you create a tree with all categories of HYPOTHESES of why it happened.

Just like we did with our executive trying to fix the customer retention problem he is facing.

(By the way, this is why you can call them “problem trees”, “why trees” or “hypotheses trees”.)

But you can also use Issue Trees to map out SOLUTIONS.

This makes them really useful.

A consultant who can figure out what’s causing a problem every single time is a pretty good asset to the team.

But to have a consultant that not only can do that, but who can also figure out the best solutions to those problems every single time is even better!

So let me show you how a “Solution tree” or a “How tree” is different from a “Problem tree”.

Suppose our Telco Executive character did NOT have a customer retention problem. Everything is fine and clients aren’t unsubscribing from this company’s services more than the normal rate.

But, naturally, they still have some level of customer churn.

Let’s say that they want to make that level even better than it is today.

And then the executive team gets together for a meeting to “brainstorm” some ideas on how to reduce customer churn rates so they can grow revenues more.

What most people in this meeting are doing is to throw ideas on a whiteboard.

“Hey, perhaps we can improve our customer service.”
“Hey, maybe we should offer faster internet.”
“Hey, what if we put people into long-term contracts?”

But our Telco Executive is smarter than that. He has learned how to make Issue Trees with his friend, a McKinsey consultant. And he puts his learnings into practice.

**Action step: grab a piece of paper and build an Issue Tree with the CATEGORIES of potential ideas/solutions this company could have to improve their customer retention.**

Now, word of warning: this “solution Issue Tree” is NOT perfect.

If you try, you can probably come up with an idea that could improve customer retention that doesn’t fit any of these categories.

And the reason for that is that it’s much harder to map out all types of possible solutions to a problem than to map out all types of possible causes to a problem.

But in case you do come up with an idea that doesn’t fit any of these categories, you can easily abstract what “type” of solution is this and then create a category for it.

Now, you might be thinking – “Bruno, why do I want to use Issue Trees for mapping out types of solutions? Why not just Brainstorm freely?”

There are three reasons for that:

(1) Your ideas are gonna be way more organized

This helps you communicate your ideas with others.

And it also helps you organize everyone’s ideas into a coherent whole.

And then better prioritize those ideas and even “divide and conquer” the implementation of them. You know, all the good stuff Issue Trees allow you to do.

(2) Creativity from constraints

This is counter-intuitive, but bear with me.

There’s significant research showing that having some constraints make people MORE creative, not less. (You can see some of the core ideas here , here and here .)

And we know that intuitively!

Well, try to create a short story in your head.

Nothing comes to mind, right?

Now try to create a short story that involves an English guy, a French woman, a train trip and a few bottles of wine.

It’s actually easier to do the second, even though there are many more constraints.

Now, if I ask you to generate ideas on how to improve customer retention in a Telco company you’ll probably be able to generate 5-7 ideas until they start to become scarce.

But if I ask you to generate ideas on how to improve customer service in a Telco you’ll also be able to generate 5-7 ideas until they become scarce. Even though improving customer service is just a sub-set of the things you can do to improve customer retention.

And then I could ask you to generate ideas on how to make it financially costly to unsubscribe and you might be able to give me a few ideas as well.

Each of the last two questions was a branch of our issue tree from above.

And because our Issue Tree above has 7 different branches, if you’re able to generate 5 ideas for each, that’s 35 ideas!

I’ve never met a person that can generate that many ideas with just the prompt question (how to improve customer retention) and without building an Issue Tree first.

Our brains seem to get confused with that many ideas.

But if you add structure (forced constraints), you can think freely about each part without worrying about missing something.

Which leads me to the 3rd reason why you will want to use “solution Issue Trees” whenever you need to brainstorm ideas…

(3) They force you to see whole categories of ideas you wouldn’t have seen before.

This takes a bit of practice, but once you’re able to see how each category fits the whole, you might see parts of the whole that you weren’t even seeing before.

Take the “Make it costly to unsubscribe” category for example.

When I came up with it, I was thinking about financial costs. You know, contracts and stuff.

But when I saw the word “financial” coming up in my mind, I immediately noticed that there could also be “non-financial” costs, such as having to go to a physical retail store to cancel the service or losing your dear phone number that you had for 8 years and all your friends and business connections have.

I didn’t have these “non-financial costs” ideas before I create the category for them.

Which is another big advantage for using Issue Trees to come up with solutions for your problems. You can see the larger picture.

So, what’s our take away from all this?

Simple. Issue Trees are a “map” to your problem that help you prioritize what’s important and “divide and conquer” to solve it more effectively.

And you can use them to map out solutions as well.

Oh, and by the way, I almost forgot…

One really powerful thing you can do is to use “Problem Trees” to find the problem and once you found it, use a “Solution Tree” on your newfound problem.

So, remember how we used a “Why Tree” to find out that one of our Telco Executive’s problems was that his customers were leaving to the competitor?

Now we could use a “How Tree” to figure out potential solutions to stop our customers from switching to the competitors even though they don’t really like us and the competitor is offering a better offer than we are.

I’ll leave this Issue Tree for you to build.

And you’ll be able to build it using the techniques you’ll learn in the next chapter!

Three Techniques to Build Issue Trees

You can have all the theory in the world, but if you don’t put it into practice you’re not gonna solve any of the world’s toughest problems (nor get a job offer at McKinsey, BCG or Bain).

In this chapter we’ll go deeply into the mechanics of how to build quality Issue Trees.

More specifically, we’ll go through three practical techniques that you will be able to apply in your next case interview or executive meeting to structure any problem.

The structure of an Issue Tree

Issue Trees are a “problem structuring” tool.

That means you can structure problems using them.

But even Issue Trees have an underlying structure to them. It gets a bit “meta” and abstract, but the point is that every Issue Tree shares some similarities with other Issue Trees.

Knowing these common characteristics is the starting point to being able to successfully build them, so I’m gonna go over that in this short section.

And I’ll be quick, I promise.

(Note: I’m gonna give names to some stuff so that you and I can talk more effectively over the rest of the guide, but you don’t have to memorize those names nor use them in case interviews.)

So we seem to always keep coming to this MECE thing, don’t we?

We have a whole article series on that , and I highly recommend you to go through it.

You can do so right now and then come back to this guide or you can read this guide first and then go there to understand how to make each part of your Issue Tree MECE.

Now, I don’t want to break your reading flow here…

So, before you open a new tab on your browser and get into another rabbit hole, let me explain what MECE is in simple terms.

MECE means Mutually Exclusive, Collectively Exhaustive and it is a basic principle of organizing ideas that was popularized by ex-McKinsey Barbara Minto (from the book on the Pyramid Principle, you might have heard of that) but goes back to the ideas of Aristotle (yes, the greek one!).

It basically means your reasoning has no gaps (Collectively Exhaustive, all parts together exhaust the whole) and no overlaps (Mutually Exclusive, one part is different and independent from the other).

Easy, right?

Well, kind of. Most problems out there are harder than drawing rectangles.

So, to give you a better idea of how to apply the MECE principle to a business problem, here’s an image from our article on The 5 Ways to be MECE of different MECE ways to break down the same problem:

No need to worry about understanding this whole image right now, but the idea behind it is that (i) there are 5 types of ways to break down the problem in the image’s title (or any other problem) in a MECE way, and (ii) you can build different structures within each type.

An Issue Tree is built using a lot of these MECE structures. You also need to know how to pick among different options when you find more than one way to break down a problem..

I’m gonna link to the article on the 5 Ways to be MECE again because it’s the best way to learn about MECE in a practical way. Instead of a bunch of theory, I show actual techniques you can apply right now to any problem in that article.

Anyway, enough with MECE. Let’s jump into the actual techniques to build Issue Trees.

Technique #1: Create a Math Tree

Math Equations are ALWAYS MECE.

Equations have no gaps and no overlaps (otherwise they wouldn’t be equations).

Which is why I used rectangles within rectangles to explain MECE above. Rectangles are huge in mathematics if I remember my high school math right.

Anyway, one easy way to create MECE trees is to take advantage of that and ALWAYS break down the next level using a math equation.

Obviously you can only do that if your problem is numerical.

But since most business problems are numerical, we’re in luck!

I’m gonna show you how to do this in a “slideshow” kind of way because I wanna show you in a very step-by-step fashion, so be prepared to click on the arrow button more than a few times:

Creating math trees as a way to create Issue Trees isn’t hard at all once you get some practice.

But some of its nuances can be deceiving. Most people see them done and think they can easily do it, but it all goes downhill when they actually grab a piece of paper and attempt to do these trees.

So, here are four methods to actually create your “mini-equations” to break down each bucket:

#1. Use a proven formula

Most of the time you don’t need to reinvent the wheel.

If you know a formula that fits the problem well, just use it!

The most common one here is the classical Profits = Revenues – Costs, but there are others as you can see on the image below…

You don’t need to memorize any formulas for your case interviews, as you can use the other methods and they will work.

But knowing some of these, especially the most basic ones does help a lot.

#2. The "Dimensional Analysis" method

This one’s my favorite!

Just find one direct “driver” of the variable you want to break down – a driver is a “fundamental cause” for that variable.

For example, one direct “driver” or “cause” of revenues is the “# of customers” you have. If you get more customers, these new customers directly cause your revenues to increase.

Then, use dimensional analysis to find its mathematical complement. If you want “REVENUES” and you have “# OF CUSTOMERS”, you need to multiply that by REVENUE/CUSTOMER.

Just like in your high school physics class, customers on the numerator will cancel out with customers on the denominator and you’ll be left with REVENUES as a metric – exactly the one you’re aiming for.

This method is amazing because it lets you break down almost any metric into a formula really quickly – the only thing to be careful with is to not lose meaning in the process and end up with a formula that is mathematically right but doesn’t make any sense to actual human beings.

#3. The Funnel method

This works wonders when the target metric is a percentage or is the end result of a funnel.

Take one example from e-commerce: Conversion Rate.

This is the % of visitors in your website that buy from you. How can you break that down?

Simple, you multiply the steps of the funnel from visitor to buyer.

Funnels are everywhere: Sales, Product Development, Process Optimization.

All you have to do is to find these funnels and then break them into stages.

#4. Use a sum of segments

This is my least favorite method because it doesn’t go too much into the structure of the problem, but simply slices it out.

However, it can be useful.

For example, if you’re working with a conglomerate and their profits are down, it might be useful to segment that conglomerate into its different businesses.

Or if you’re trying to understand a company’s market share drop in a certain category, it might be useful to just break it down into the market shares of its product lines.

If you’ve read the article on the 5 Ways to be MECE and you’ve been paying attention, you might have noticed that method #1, “Using a proven formula” and #2, “Dimensional Analysis” will get you an Algebra Structure.

Method #3, “The Funnel Method” will get you a Process Structure.

Finally, method #4, “Sum of segments” will get you a Segmentation type of structure.

If you haven’t read the article, don’t worry about these names – they are some of the ways to be MECE we teach there. I’m just helping the folks who did read it already to make the connections.

So, summing up. You can use any of these four methods to create a “mini equation” and you combine these “mini equations” to create a “Math Tree”, which is the first technique to build and Issue Tree.

And it’s a technique that works great with numerical variables, but doesn’t really work if you have a different type of problem to solve.

So, to tackle non-numerical problems – or even to make better Issue Trees for numerical problems – let’s move on to the most powerful technique in your Issue Tree toolkit: layering the 5 Ways to be MECE.

Technique #2: Layering the 5 Ways to be MECE

Technique #1 works great because math is ALWAYS MECE and because creating equations isn’t too hard.

But not every problem is numerical and can be structured using equations alone.

And even to those problems that are numerical, doing a Math Tree isn’t always the best way to go about structuring them.

Here’s where Technique #2 comes in – instead of layering “mini equations” on top of each other, we’re gonna layer “mini MECE structures” on top of each other, regardless of them being equations or not.

Remember, we were confident to use math equations to build Issue Trees because they are always MECE. But from first principles what we need is MECE structure, not necessarily mathematical ones.

And where are we gonna find these “mini MECE structures”?

Easy, with the 5 Ways to be MECE. These are 5 specific techniques we’ve developed that guarantee a MECE structure.

I’ll make your life easier in case you want to read about that now and link to the article we wrote about them.

But here’s a quick recap:

The process of building Issue Trees by layering the 5 Ways to be MECE is itself very very similar to the process to create Math Trees.

Step #1: Define the problem specifically (no need to be a numerical variable here).

Step #2: Break down the first layer using one of the 5 Ways to be MECE.

Step #3: Get to the 2nd (and 3rd, and 4th) layers by breaking down each bucket into another “mini MECE structure” that comes from the 5 Ways to be MECE as well.

I’ll show you the exact process to create an Issue Tree by layering the 5 Ways to be MECE through the example below:

Layering the 5 Ways to be MECE is my go-to method to create Issue Trees and break down problems or finding solutions.

I use it every day of my life, either on paper or just in my head.

And I used to use it everyday when I worked at McKinsey as well (even though I was doing it unconsciously – no one there had explicitly told me there were five ways to be MECE).

Now, let me address one thing that comes up often… One thing that may have crossed your mind as you were going through the three steps above regarding the Issue Tree is “well, but this is so obvious” .

That thought may have crossed your head in each break-down of a bucket or just when looking at the whole Issue Tree.

And here’s my take on it: a well-structured problem SHOULD look obvious – at least in hindsight .

How Elon Musk changes the world structuring problems in "obvious" ways

(I swear to you it’s interesting, but you can skip this green box if you want and/or understand why MECE Issue Trees are super important even when they’re “obvious”)

You’ve probably heard of Elon.

In case you haven’t, he’s this guy…

And he’s created these companies…

So, the guy basically transformed the payments industry, the automotive industry, the aerospace industry and is transforming the tunneling and the solar power industry.

But how does he do that?

Well, anyone who does that much has many “secret sauces”, but one of the special things Musk has is to think things from first principles.

In this fantastic blog post (from one of my favorite blogs), a guy who had access to Musk breaks down exactly how he thinks.

But let’s analyze one specific instance: how he came up with “The Boring Company”, a company that was created to dig tunnels more efficiently and solve the traffic problem in Los Angeles.

There are two underlying logics to the company:

Simple logic, but a really strong reasoning about why tunnels are probably the best way to solve the traffic problem.

(And it actually is the only way that’s ever worked so far – demand for roads keep increasing no matter how many Uber rides people take, building more roads doesn’t seem to make a difference in most cities and no one’s ever been able to make flying cars… But most people in large cities take the subway/metro system every single day.)

Notice that we’re basically dividing the problem into supply and demand and then dividing “road” capacity into on ground, flying and underground.

There’s no rocket science here (pun intended).

Alright, but there’s still a problem with tunnels: they’re expensive to make. So, is it possible to make them cheaper? Here comes Elon’s Logic #2 to build The Boring Company:

Again, no rocket science here (although a bit of tunneling science).

If you want to understand better how Musk thinks, I recommend this article and this TED Talk .

Now, onto what matters for us:

(1) Most traffic specialists know that trying to reduce demand is an uphill battle and that expanding road capacity is mostly fruitless.

(2) Most people in the auto/aerospace industry know that flying cars are a very far away dream

(3) Most people in the tunneling industry understand the cost drivers of a tunnel.

And yet, no one looked at the big picture and questioned things from first principles.

You need an Issue Tree to do that, even if it’s an obvious one.

I’m not saying Elon Musk draws Issue Trees for a living, but I know he has them in his head because he talks like he has one – I “took” both trees I showed you above from his own words.

Takeaway from the green box: Issue Trees are “obvious” because they’re drawn from first principles.

And this means if you want to think from first principles, draw Issue Trees.

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Technique #3: creating decision trees.

In the realm of Microsoft Excel, the most basic kind of logic you can do is using math operators. That is, adding, subtracting, multiplying and dividing.

If you wanna go a step further you can use what they call “boolean operators”: AND functions, OR functions and so on.

And if you want to go a third step further, you can use “conditional operators”, the most famous of which are IF functions.

Decision Trees are basically regular Issue Trees with “conditional operators”, IF-THEN functions.

Now, let me translate into plain English for all the non-Excel nerds out there…

(Or should I say “future Excel nerds? I mean, this is a site for aspiring management consultants!)

When you do a Math Tree, the only way you have to relate the variables to each other is through math symbols. E.g.: Revenues = Price * Quantity. There is a mathematical relationship among everything in your Issue Tree.

It is great to have math because math is always MECE, but it is also limiting. What about everything that can’t fit an equation?

Enter Technique #2: Layering the 5 Ways to be MECE.

If you pay attention to it, everything that’s not in a mathematical relationship in that technique is joined logically by “AND” or “OR” relationships.

For example, we can find better employees ‘at the schools we already recruit in’ OR ‘in new schools’.

Another example, we can make new recruits better before their first project ‘by training them before they start’ AND/OR ‘as soon as they start working for us’.

Decision Trees are just like regular Issue Trees but they add another layer of logic to it: IF-THEN statements.

I won’t go into too much detail on how to build them because (1) it’s an advanced skill to be able to anticipate all the if-then logic required to take a decision before you even start exploring the problem, and (2) you don’t need to be able to do this to get a job at McKinsey, BCG or Bain if you can use the other two techniques well.

But I will give you a simple example below so you can see what I mean.

And if you want to learn more about this, here’s a timeless article from Harvard Business Review on Decision Trees.

There are also different types of decision trees.

For example, you can create a decision tree for an investment opportunity that considers the probabilities of different events to happen in order to calculate the expected value (there’s an example of this in the HBR article I’ve shared above).

Or you can create decision trees for WHY and HOW problems where you use IF-THEN statements to say where would you focus and prioritize if certain conditions applied.

(An example of the last phrase is this: in a case on “How should a restaurant grow revenues”, you can say that IF it has lines/too much demand, THEN you would focus on increasing capacity through expansion or increased productivity, and that IF if doesn’t have enough demand, THEN you would focus in customer acquisition and retention initiatives.)

Decision Trees can get really complicated even for simple decisions, so I would NOT recommend you to start learning with them.

Focus on Techniques #1 and #2 to solve WHY and HOW problems.

For “decision-making”-type problems, we recommend you to learn Conceptual Frameworks first. We teach how to structure these problems using Conceptual Frameworks in our free course on case interview fundamentals.

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Six Principles for AMAZING Issue Trees

Man does not live by bread alone.

And Issue Trees need more than being “technically correct”

If Issue Trees had a “soul”, it would live in the six principles outlined in this short chapter.

In fact, if you follow the principles from this chapter, you don’t even need to use any of the three techniques I showed you on the last chapter.

And if you MASTER these principles, you might be able to come up with your own techniques.

(And if you do come up with a “fourth technique”, please shoot me an e-mail telling me about it).

Separate different problems early on

Some restaurants that want to grow revenues should work on getting more clients. Others have too much demand and should work on expanding their operations to handle that and sell more.

Most companies that have employee attrition problem have some problem that makes people wanna leave their jobs. Others are just firing too many people.

And a violence crisis in a country could be caused by criminals. But it could very well be caused by a really violent police system as well.

The common factor between the last three situations is that each one could be caused by two COMPLETELY DIFFERENT PROBLEMS.

Separate them early on your Issue Tree because trying to fix the two things together will only lead to confusion. Not good.

Build each part ONE AT A TIME

Most people who see a huge Issue Tree for the first time are overwhelmed.

Of course they are!

They see this huge logical structure (that takes time to digest) and wonder if they’ll be able to do the same when they need to.

What they’re missing is that these trees are built one step at a time .

First you get the problem question and your only concern is to define it well.

Then your only concern is to break it down into a first layer.

Then you get each bucket from the first layer and your only concern should be to break each down into a “mini MECE structure”.

One bite at a time, you will eat the whole metaphorical elephant.

Each part must be MECE

I’ve talked about MECE before in this article, but I’ll do it one last time.

ME = Mutually Exclusive = No overlaps between the parts of your structure = your structure is as clear as the blue sky for another person to understand.

CE = Collectively Exhaustive = No gaps in the way you break each part of your structure down = your structure is rigorously correct.

MECE is tough for most people, but you can use the 5 Ways to be MECE as a checklist of structures you can use to be MECE.

That means it’s not gonna be as hard for you and you have more chances of getting the offer than the other people. Good for you!

Each part must be relevant and add INSIGHT to the problem

There are many MECE ways to break down any problem.

Choose the one that’s more relevant. The one that adds more insight to the problem.

For example, one of the Issue Trees from Chapter 2 was about improving the quality of new recruits in a consulting firm. Within “making the selection better”, I could’ve broken it down into “Stages 1, 2, 3” and so on of the selection process.

That would’ve been “technically correct” and “MECE”, but it would bring absolutely no insight to the table.

Because it wouldn’t be problem-specific .

Here are two resources to help you make your structures more insightful and problem-specific:

The first is a Youtube video on how to make better revenue trees. It shows how to create more insightful revenue trees but you can apply the same principles to any type of Issue Tree.

The second is “The Toothbrush Test”, a numerical measure so you can get a proxy of how insightful one structure is compared to another. You can watch the video here or read the article here .

Each part must be eliminative and help you FOCUS to the problem

An Issue Tree that is built in a way that allows you to ELIMINATE all the non-problems and focus on the one thing that’s driving the issue is way more useful than one that does not allow you to do that.

Say you’re a soft drinks company concerned that you’re selling less soda.

Here are two ways to structure the first layer of that Issue Tree:

(1) Drop in general soda consumption OR Drop in market share

(2) Customers less willing to buy product OR Competition getting stronger OR Company doing poor marketing or supply chain OR Distribution channels not exposing our product

Which one’s better?

Well, according to this fifth principle, (1) is better because it allows you to get data and eliminate a whole branch (unless the problem comes from both, of course).

Eliminative Issue Trees help you FOCUS the problem and waste less time (that means more 80/20 for you).

The key to be eliminative is to make each bucket FALSIFIABLE.

Falsifiable means you can find a test that, given a certain result , guarantees that the problem is not on that bucket.

This falsifiability is what makes Issue Trees “hypothesis testing” structures. If you want to be a hypothesis-driven problem solver you need to include falsifiability in your structures whenever you can.

However, this does not mean every single structure you create must follow this principle.

There are times where falsifiability is impossible , and that means you should focus your efforts in being the most insightful as you can (Principle #4).

It is usually in these situations where you’ll want to use a qualitative, conceptual framework. You can learn more about this in the free course we offer on case interview fundamentals. In the Frameworks module of the course we will show you exactly when to use conceptual frameworks and how to create them.by

Clarify what you need in each layer you build

You might be shy, but hey, overcome that shyness!

You don’t need to do guesswork to build your structures. You can ask first.

Actually, doing guesswork when you could’ve asked a simple question and eliminated confusion will harm your performance.

Say you’re breaking down how a consulting firm could hire better junior consultants. You’re trying to break down how they select candidates, but you’re not sure how their recruiting process is currently like…

Say to your interviewer:

“Hey, I want to break it down into the stages of the selection process but I don’t know what those stages are. Here’s what’s on my mind… Does it make sense or did I miss something?”

If you’re doing Issue Trees to solve a problem in your work, this principle is even more important. You can’t structure what you don’t understand, so when in doubt ask questions and understand it better!

Sometimes these principles will enter in conflict with one another.

You might need to choose between being more eliminative and being more insightful.

You might feel in doubt of whether you should be fully exhaustive (MECE) or just add the relevant stuff.

And when principles enter in conflict, experience and judgement are here to save the day.

Seeing real examples of real people that know what they’re doing making Issue Trees to solve case interview problems is invaluable to get that experience.

Which is why I will show you in-depth examples in the next chapter, including videos of me going through the thought process of building Issue Trees with you.

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When I was preparing for my case interview I looked for good Issue Tree examples all around.

I found none .

I don’t want you to go through the same, so here I’m gonna go all in and not only show you great Issue Trees but also show you, in video, how I think through each step of building them.

I’ll show you everything that goes through my mind as well as the specific nuances that make them great.

I will use different examples so you can see how the principles and techniques apply to different types of situations.

And I will do exactly what I’d do in a real case interview on when solving a real problem at work.

The only thing I’ll avoid doing is using Decision Trees.

Because it’s much much harder to get to a MECE result using them, let alone explain why it’s MECE. I’d be only showing off instead of actually helping you learn how the principles apply and what makes a great Issue Tree.

Not my style!

Example #1 - Airline fuel costs surge

This first example is of a fairly easy case question that would lead many well-prepared candidates to failure.

It’s funny how some problems can be easy to real consultants and yet hard even for candidates who have done 50+ cases.

Here’s why this happens: the business problem isn’t hard to solve from a first principles perspective (which is how good consultants tend to think) but they’re a bit unusual or too specific to an industry.

Most candidates who haven’t internalized the principles of solving problems well feel overwhelmed when they get a case completely unrelated to anything they’ve seen before.

Even worse is when this problem doesn’t fit the half a dozen frameworks these candidates have memorized by heart.

Here’s a video of this first example. I highly recommend you to try to structure this Issue Tree by pausing the video right after I clarify the case question and then compare your structure and your thinking process with mine.

If you don’t have access to audio or can’t watch a video right now, you’ll be able to keep reading and grasp the main insights as well, although I highly recommend you come back to watch this later!

So, what’s interesting about this Issue Tree example is that I have structured the first two layers of the tree as a Math Tree (Technique #1) and then I used the “Opposite Words” technique and the “Conceptual Frameworks” technique to build layers 3 and 4.

You can do that too!

Here’s the whole Issue Tree if you weren’t able to watch the video:

There were three main take aways from this structure:

Takeaway #1: Break down a numerical problem mathematically as long as the math remains meaningful/insightful – then get more layers using qualitative “mini-MECE-structures”

As with most thing problem-solving related, this is not a rule written in stone.

There are a few numerical problems that are best structured with a qualitative structure. And you don’t always need to do the qualitative layers afterwards.

But usually the best way to break down a math problem initially is to break it down into an equation first, as you’ll be able to quantify how each driver contributed to the problem.

And usually the equation alone won’t be enough to bring you to the meaningful stuff.

In this case, for example, if we were only mathematical in our structuring we would have missed important elements that show real world business intuition, such as “maintenance”, “aircraft weight” and “mix of aircraft in the fleet”.

Takeaway #2: Stop each branch when it can reasonably explain the source of the problem

I have stopped some parts of my tree in Layer 2, other parts in Layer 3 and others in Layer 4.

How did I make this call?

A lot of people have asked me this in the past: how can I know that my Issue Tree is done? How many layers do I need?

The rule of thumb is to stop when your buckets can reasonably explain the problem.

For example, on Layer 2 you have a bucket which is “# of trips flown has risen”. This can reasonably explain why fuel costs might have risen. It’s pretty logical – if you fly more trips, your fuel costs will rise as well.

Now, one could ask “why has the # of trips flown risen” and if that’s the actual problem going on, I as a consultant would want to know that. But that’s getting granular, you don’t need to go that far unless the problem is proven to be there.

If I told my mom or someone on the street that an airline’s fuel costs have risen because the # of trips have risen, they’d accept the answer and probably not question it further (and they certainly would tell me I’m a weirdo for caring about an airline’s fuel costs).

Now, if I told my mom or a random guy on the street that fuel costs have risen because liters of fuel per km flown have risen they would: (1) think I’m really really weird, and (2) not take that answer as it is.

Even if I used more accessible language and said that this airline’s fuel efficiency was down, they’d still ask me “why is it down”? (That is, assuming my mom is actually interested about airlines).

If I had stopped that branch on the 2nd layer, I wouldn’t be telling the whole story.

And so I went a level deeper.

Now, on the 3rd layer if I say that fuel efficiency is down because we’re using less efficient types of aircraft, most people would be satisfied with that answer. I can stop the Issue Tree here.

But in the case we’re flying the same aircraft, most people would NOT be satisfied. They’d be like “Hey, you’re telling me you’re less fuel efficient even though we’re flying the same aircraft? How come?”

And so we dig a level deeper on that one. Maybe the aircrafts are flying with more weight. Or we’re doing less maintenance. Or we’re flying at lower altitude and facing denser atmosphere. Or our pilots are changing speed all the time.

Most people would take any of those as sufficient answer. Which means we don’t need to dig a level deeper.

Takeaway #3: You can still go deeper in the buckets you need

If the last take away gives you an idea on where to stop structuring the Issue Tree, this one gives you permission to dig deeper than that.

Say your interviewer tells you the problem is that this airlines is flying their planes heavier and asks why that might be. Well, weight was at the end of our tree, right? But we can still investigate the reasons behind that increased weight.

Here I would segment the things that add weight to airplanes into their categories: people, cargo, equipment, fuel itself (we may be flying with excess fuel and thus spending more fuel to carry fuel itself).

Or say that the interviewer tells you that fuel prices have gone up even though we’re buying the same product from the same supplier.

Why that might be happening?

Well, either this supplier’s cost has gone up (because crude oil is up in price, for example) or their margins are higher (because we’re not negotiating as well, for example). We could dig deeper into each one of these factors if need be.

The point here is that even though you need somewhere to stop your Issue Tree (otherwise you’d spend the whole day building 15 layers), you also need to be aware that you can go as deep as you need to in the specific parts of your structure that the problem really is.

You find where the problem really is by getting data, numerical or not, for each part of your structure.

Example #2 - Overwhelmed consultant productivity

Real consultants have their own personal problems to solve as well.

And often time they will solve them with Issue Trees!

They’re a great way to see what your options are.

So before you look into this example, I want you to do an exercise:

**Action step: grab a piece of paper and write down all ideas you have to become more productive in case you were overwhelmed with work as a consultant**

What you’ll see from this exercise is that if you just “freely brainstorm” ideas to improve productivity on paper, you’ll end up with a huge list of (probably) unconnected action steps that are hard to estimate impact and to prioritize.

But if you had built an Issue Tree to organize those ideas , you’d get something much closer to an actual system to improve productivity.

Here’s what I mean by that:

This tree is solving a more qualitative problem than Example #1, but the techniques still work.

You define the problem really specifically at first.

And then you layer different “mini MECE structures” using the techniques from the 5 Ways to be MECE.

Here’s the final Issue Tree in case you couldn’t watch the video:

Of course your tree can still be different than this one and still be correct.

How do you know if it’s correct or not?

Well, simple: are you adhering to the key principles? Are you using the techniques I have shown you in this guide?

If so, your Issue Tree is good to go!

Example #3 - Help a government solve illiteracy in children

This is an interesting example because it focuses specifically on Principle #1: Separating different problems early on.

In fact, the whole Issue Tree is built by separating different problems over and over again.

Because the problem to be solved has many different possible root-causes that are completely different from each other.

Once you watch the video, you’ll see that the way the Issue Tree is constructed in a very intuitive way.

However, give this problem to most people and they aren’t able to structure it. They’ll spit out ideas and hypotheses without order nor an overarching logic.

Check it out how to help a government solve illiteracy in its children that go to public schools:

If you couldn’t watch the video, I’ll put an image of the Issue Tree bellow.

Notice how each layer is basically the previous bucket divided into two completely distinct problems.

The value of building Issue Trees like this is that you get a map of all types of possible root-causes. It’s also pretty easy to do so!

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Common mistakes and questions.

I’ve helped hundreds of people learn to build Issue Trees.

In the process I’ve seen them making thousands of Issue Trees. And probably somewhere north of tens of thousands of mistakes.

Making mistakes if part of the learning process.

But you don’t have to make all those mistakes yourself because you can learn from theirs!

In this chapter I will show you the most common mistakes people make (with real Issue Trees, from real candidates) and also answer some of the most common questions that arise as you learn to build them.

What you can learn from the key mistakes of real Issue Trees from real candidates

When I first wrote the 5 Ways to be MECE article I had a little challenge in the end of it.

I challenged people to send me a structure for a specific business problem that could happen in a case interview:

“Imagine you’re doing a project with Amazon and they’re complaining about a surge in theft in their warehouses – what could be causing this surge in theft?”

And so I got dozens and dozens of real Issue Trees from real candidates for the same problem.

What’s fascinating is that all these candidates had three things in common:

(1) They were having trouble with creating MECE structures for their cases (or else why would you read a huge guide on how to be MECE?).

(2) They had just read a huge guide with different techniques to be MECE and instructions on how to build Issue Trees using these techniques.

(3) They were dedicated enough to take my challenge, spend 10-20 minutes building their best Issue Trees and sending them to me.

Still, even with all those things going for them, most of their Issue Trees had mistakes. Mistakes you and I can learn from.

So in this section I’m gonna show you their trees, point out their key mistakes and show you the feedback I sent them.

#1 - Anastasia and the sin of ignoring problem definition

The first Issue Tree I wanna show you was sent by Anastasia.

Here it is:

Seems like a quite good Issue Tree, right?

I mean, it describes quite well the process of a warehouse.

Well, not quite.

There are a few mistakes that this Issue Tree makes in terms of MECEness, some parts could be more insightful, etc. But the most important mistake here is that Anastasia ignored the specificity of the problem.

Much of this Issue Tree isn’t about theft – it is about losing items in general. So she’s talking about damage, negligence, machine mistake, etc.

Go back to the image above and click the right arrow to see all the areas of this tree that are not about theft at all.

Most of the tree is not talking about theft at all!

What that means is that she’s talking a lot about things unrelated to the problem and leaving a lot of important things out. It also implies that she wasn’t listening to the problem.

This is the #1 thing I’d tell Anastasia to focus on and the #1 thing I’d tell you to make sure you’re not messing up.

Now, Anastasia’s structure also has a #2 thing that I’d tell her to focus on if problem definition weren’t a problem: look for root causes.

While she makes an excellent description of how the warehousing process is and thus is able to map out where the problem might be, she never talks about the why.

You know, things like security systems and lack of penalties and having warehouses in areas with a lot of crime. The types of things you might expect for a WHY question…

#2 - How Anne messed up with layer ordering

This Issue Tree is actually quite good!

But it has three main mistakes. Can you guess what they are?

Well, I gave you the main one in the title.

Anne’s first layer shouldn’t be a first layer.

Because geographical location is not all that important. The different geographical areas of the problem aren’t the most relevant way to break it down.

What’s more, even if it were, why divide by continent? Why not small vs. big cities? Or low income vs. high income areas? Or high-crime vs. low-crime areas?

Anyway, I think it’s an excellent idea to mention that you’d like to see in which warehouses is the problem more prevalent. But what I would’ve done is to put that as a side note to an Issue Tree that actually digs into the potential causes of the problem, not as the main course.

She could’ve done an Issue Tree of causes for one warehouse and then said at the end: “and then I’m gonna look at these causes for all warehouses we have, segmented by geographical area, warehouse size, how old they are, etc”.

And what would this Issue Tree that digs into the potential causes look like?

Well, very much like Anne’s example Issue Tree for American warehouses (which I guess she would replicate for other continents as well).

Now, you might be thinking: what are the other two mistakes she made?

Well, one is that she offered solutions to each root-cause of the problem. That’s not a mistake in itself. In fact, I loved it. But the problem is that she was a bit too early on that – she should’ve gone a layer deeper into the why each thing happened.

Keep in mind the case question was a WHY question and not a HOW question.

And what she did was to suggest, for example, that if internal thieves who had the intention of stealing were responsible for the surge in theft, then they should run better checks.

What she should’ve done instead was to say that if that was the cause, then that caused happened because (a) they’ve stopped doing background checks, (b) background checks have worsened in quality or (c) background checks were never good at stopping that but that was never a problem beforehand. And then perhaps dig even deeper into the cause.

But she offered solutions before she got to the root cause, and that may hurt because she may be solving the wrong problem.

And the last mistake she did was one related to problem definition.

Everything she mentioned was related to the amount of theft. But we don’t know if that’s the problem. It’s not clear on the case question (on purpose). Maybe the problem is the value stolen.

So, she would’ve done much better by showing that in her structure. Maybe there are more thefts (in which case her issue tree is valid) and maybe the amount stolen per theft is higher (and because she didn’t consider this, she missed a whole part of the problem).

#3 - Guillaume and the "aggregator fallacy"

There are many problems with the Issue Tree below, for instance:

A regional segmentation early on when that’s not a really relevant factor to explain the problem (as in Mistake #2)
This regional segmentation isn’t even MECE (there are emerging countries in Europe and he forgot all developed countries in Asia)
A lack of $ value of theft (again, as in Mistake #2)
The way he breaks down a process structure to explain a surge in # of thefts per warehouse isn’t very insightful/relevant

But I want to call your attention to one other mistake which is related to causal effects. I call it “the aggregator fallacy”.

Can you spot it?

Let me ask you one thing… If the number of gas stations raise in a city by 2X in a year, will sales of gas increase by 2X as well?

Will they even increase by 10 or 20%?

Not necessarily!

More gas stations don’t drive more demand for fuel (unless there’s very few, high priced gas stations in town, but let’s leave extreme scenarios aside).

Yes, there might be 2X the number of gas stations because demand skyrocketed. But it could also be the case that gas stations were a really profitable business and entrepreneurs entered this market even thought there was no increase in demand.

It could also be the case that some people who don’t know what they’re doing entered the market even though demand didn’t increase and profits weren’t that high (and everyone’s losing money now).

So if you were to find out if demand for gas increased in a town one MECE structure you could use is “# of gas stations * avg. amount of gas sold per station”, but that wouldn’t be the best one.

Because # of gas stations don’t drive demand – more cars and more usage per car does.

The same thing is happening with Guillaume’s structure.

More warehouses don’t drive more theft. They don’t cause more theft.

Say, for example if Amazon had restructured their operations and they had switched from 10 huge warehouses to 100 smaller ones, with the goal of having faster delivery. Would it be ok for theft to increase 10X? Would it even be ok for it to increase by 50 or 100%?

Probably not, right?

Amazon’s carrying the same number of items, they have roughly the same number of employees (considering internal theft) and if they have their security systems in place, they’re not necessarily more attractive to external burglars (if anything, it’s harder to steal a smaller warehouse than a huge one).

More warehouses shouldn’t cause more thefts. The warehouse is not a driver of stealing just as the gas station is not a driver of demand for gas.

The warehouse and the gas station are merely aggregators of something. The warehouse aggregates products to be shipped (or stolen) and the gas station aggregates fuel to be sold (or not sold in case of a flat demand).

Which is why I call this mistake “the aggregator fallacy” – thinking that because the aggregator has increased that it has caused your problem.

Instead, try to build your Issue Trees with some causal relationship in mind. In the case of the gas station problem, that’d be “# of cars * fuel used per car”.

In the Amazon theft case, you could use “# of products in the warehouse * theft rate” if you assume that more products cause more demand for burglars or “avg. crime rate where Amazon warehouses are located * % of those crimes that are in Amazon’s warehouse” in case you assume that overall crime rate is a given and you can only control your exposure to it.

#4 - Jimi, the unMECE

Again many problems with this Tree.

You can mistake-hunt later at your own pace, so I’ll just point out to the ONE FATAL MISTAKE YOU SHOULD NEVER MAKE:

Jimi wasn’t MECE on the first layer of his Issue Tree.

In part because he insisted on using a conceptual framework (the hardest of the 5 Ways to be MECE) without needing to do it (as a theft problem is a numerical problem).

In part because he didn’t know how to create a MECE conceptual framework (as we teach in our courses).

And this would’ve gotten Jimi rejected from a real case interview at McKinsey, BCG, Bain or any other firm.

And it would probably get him fired if he was in charge of Amazon’s warehouses.

Don’t be like Jimi.

Always be MECE (and especially so on the first layer)!

#5 - Was Natalia rejected due to a simple mistake?

I actually like this Issue Tree quite a bit.

It’s well built, although there are a couple of problems.

And it’s interesting because Natalia, the lady who built this tree had been rejected from a Bain and a BCG final round before. She was preparing to try again. That means she was good enough to actually get to the final round but made some mistakes that prevented her to get the offer.

Maybe her mistakes were showing in her Issue Tree?

Perhaps… Let’s take a look:

There are two great mistakes with this tree.

One we’ve talked before – Natalia went for a conceptual structure to break down the “Warehouse facility factors” bucket and had trouble building it. There’s overlap between “Security” and “Information Confidentiality”. Also, there are many things not considered here (including theft caused by internal employees).

But the one mistake I wanna call your attention to is much less obvious. It’s more a nuance than a mistake.

It is on the first layer.

The way she build it is much better than many alternatives: there’s external factors (crime) and internal factors (the warehouse itself).

HOWEVER, it’s really really tough to test which one is causing the surge in theft. These things look measurable but they’re not really.

Because measuring overall crime is a pain. And getting that data, an even higher pain.

Just to give you an example: what crime data should we consider to prove/disprove the fact that external crime has risen? Should it be overall criminal incidents? Thefts only? Warehouse thefts, specifically?

Also, how regional should the data be? Neighborhood? City? State?

And because you can’t measure “warehouse facility factors”, it’s hard to exclude a whole branch of the tree. Which means this tree is not very “eliminative”, because the factors in the first branch aren’t falsifiable.

Now, I’m being really picky here just to make a critical point to you.

Maybe in a real interview Natalia would’ve been able to come up with a test that would reliably eliminate a whole branch.

And maybe the problem could be solved without that kind of rigorous testing (e.g. maybe they completely switched their security personnel and had security holes in the process, so the cause would be obvious).

But if the situation was harder, more nuanced it would be tough to Natalia to actually diagnose the issue.

And whether she would be able to actually do it in real life is the #1 question in the interviewer’s mind.

Her first layer is not bad, but there are other MECE structures as insightful as this one that would also be more testable, more falsifiable.

And in a final round that could make all the difference.

Commonly Asked Questions

Learning from the mistakes of others is a great way to accelerate your learning curve!

But still, you might have some questions in your head.

Here are some of the questions I have been asked about Issue Trees throughout the years (and the best answers I have to those)…

Issue Trees are one structuring technique but they’re not the only one.

So there are actually two questions within this one: (1) How do I know if I should use a structure to solve the problem and (2) How do I know if I should use an Issue Tree or another technique.

Great questions!

Let’s start with #1…

You should use a structure to solve a problem, well, when you want to solve it in a structured way.

And when’s that?

Well, whenever you want to be able to foresee the steps to the solution of the problem.

That is, when you must have a due date of when the problem’s going to be solved (which is whenever you have a boss or a client, for example) or when you want to distribute the problem for other people to solve it (your employees or an outsourced company, for example).

That means almost always, especially in the professional world, where people have bosses, employees and clients.

Question #2 is a bit trickier to answer…

There are other structuring techniques – ways to break down the problem – that you can use. So, when to use Issue Trees and when to use the others?

Basically there are two scenarios: either you want to split the problem into components of the problem, or you want to look at the problem from different angles/points of view without actually splitting it.

If the first, use an Issue Tree; if the last, use another tool (such as a conceptual framework, as we teach in our free course on case interviews).

How to know which one you want is a bit more complicated and would take an article on its own to explain.

If you want the full details, check out our free course that you can find in our homepage or throughout this article, but here’s the long story short: if you want focus, efficiency and logic onto a well-defined problem use an Issue Tree and if you want awareness and insight onto a messy problem, use a tool like a conceptual framework.

A lot of people who teach case interviews say you should start with a hypothesis.

And they say that because MBB consulting firms (MBB stands for McKinsey, BCG and Bain) work in a hypothesis-driven approach. That means they come up with hypotheses and test them to find the truth (much like in the scientific method).

Being hypothesis-driven is tricky because you also have to be structured and MECE.

So, how do you make your hypotheses MECE?

Well, one way some people figured out is to build a MECE tree and just throw the word hypothesis around. If it were in a case investigating why profits have fallen, this would sound something like this:

“My hypothesis if that profits have fallen because sales are down. To know if that’s true we need to look at sales and costs.”

Notice how there’s ZERO value add to using the word “hypothesis” in the phrase above. If the guy had just asked for sales and cost data he’d ask the same questions, do the same analysis and reach the same conclusion.

If you just want to use the word hypothesis like that, go for it, but there’s absolutely no need to do it. If your buckets are MECE and testable with data, you can just lay out your Issue Tree with no “hypothesis” and test the buckets.

However if you can’t make your structure MECE/testable, you might need to use a hypothesis, but it’s a completely different type hypothesis than the one I’ve shown you above. Instead of being just a random guess with the word hypothesis on it, it must have a structure which we teach in the “Hypothesis Testing” module from our free course.

Great question, glad you asked that!

Clarifying questions are the questions you use to define the problem so you can create your structure / Issue Tree.

You use them to understand the problem better.

If the answer to a question you ask could potentially lead you to solve the problem then the question is a part of the structure of the problem and should be within your Issue Tree.

Drawing Issue Trees on paper is good practice whether you’re in a case interview, helping a client or solving your own problems.

The reason for that is that having it on paper makes it easier to communicate the ideas and frees up space in your mind so you can actually think about each part of the problem.

Not drawing the tree is kind of like memorizing a map – it’s helpful, but the whole purpose of the map is to be there when you need it without you having to know anything by heart.

But drawing does take a bit of time and in answering certain questions in case interviews, interviewers want you to be quick and may even ask you not to use paper . THIS DOES NOT MEAN YOU’RE ALLOWED TO BE UNSTRUCTURED.

It basically means they want to see if you can be structured and communicate your ideas in a structured way even when you don’t have a lot of time to think through a structure and draw it on paper.

Issue trees are a representation of how a consultant thinks. That means consultants think in Issue Trees .

They communicate using these trees as the underlying structure of the ideas they’re thinking through.

So if you don’t have time at all to think, you don’t have to draw your Issue Tree on paper, but you still must communicate as if you were going through one.

This is a super common question, and a highly context dependent one.

If you’re in an interview and it’s a more conversational, back-and-forth style, you should use less layers and get data so you know where to focus on (and dig deeper on that one).

If you’re in a more structured rigid interview format without a lot of back-and-forth, you should use more layers and they may never give you data.

The first scenario will typically happen at BCG and the second at McKinsey. Other firms will depend more on office / interviewer.

But this is not a rule. I’ve gotten the first scenario at McKinsey (final rounds) and the second at BCG. This means you’ll have to feel the situation a bit, or even ask the interviewer what they prefer.

But there’s a rule of thumb: no less than 2 layers and no more than 5 layers, regardless of format.

Because with just one layer you’re not really structuring the problem. You’re not showing a map of the situation. And with more than 5 layers the time it takes to build each layer grows while the value each layer brings diminishes. Your interviewer can always ask you to dig deeper in a certain bucket if they want you to (and they often do).

That’s true!

Drivers are “underlying causes”, and Levers are “potential things you can do to fix the situation”.

You use drivers for WHY problems and Levers for HOW problems.

If you build a good WHY tree and a good HOW tree for the same problem you’ll see the similarities and differences between drivers and levers (and you can actually go back to Item #4 in Chapter 1, where I did just that).

Simple example: if costs in a factory have increased and you want to decrease them, “material costs” could be a driver of the problem AND a lever to solve it, “taxes” could be a driver but not a lever (because you can’t change it) and outsourcing could be a lever to solve it but not a driver of the problem.

Drivers must be potential causes to the problem and Levers should be under your control.

If each part is MECE, your structure is MECE.

To know if each part is MECE, read the 5 Ways to be MECE .

And to know if your conceptual framework is MECE, check out our free course on case interview fundamentals.

Also, don’t obsess too much. There’s usually a bit of overlap between areas and no framework is FULLY exhaustive. You want to aim for “as MECE as possible”, not perfection.

Take their hint and go do it!

Interviewers are there to help you. If they tell you the problem is elsewhere, it probably is.

That doesn’t mean there’s absolutely nothing happening in the parts of the structure you were working on, but it does mean that they want to test your problem solving skills in the other part, not in the one you’re at.

If you got stuck, it’s either building your issue tree or using your issue tree.

If you got stuck building your issue tree, that means you need more and better practice. There’s a whole section on how to practice in this guide (and it’s the part that’s coming next).

If you’re in the interview already, however, there’s no time left to practice. So, what do you do?

My advice: keep it simple.

Take a breath, rethink the case and create a very simple, down-to-earth structure that can solve the problem. Not a good time to be sophisticated and elaborate when you’re stuck.

Now, if you already have your tree and you got stuck using it, here’s what you should do:

Eliminate as many parts of your tree as possible and find out everything that is NOT a part of the problem .

It’s much easier to say something is not a problem than to say for certain that something else is.

Use this process of elimination to your favor. Doctors use it all the time to save people’s lives (they call it a differential diagnosis) and you can too to save your own butt in your interviews.

How to Practice Issue Trees

Practice makes perfect.

Or, as a teacher used to say, “Practice makes permanent”.

(Which means poor practice is worse than no practice).

You can have all the theory in the world, you can have seen all the examples and still not be able to perform when the time to use this tool comes.

Which means that reading this guide is useless if you don’t apply it into practice.

In this chapter, I’ll show you how.

4 ways to practice Issue Trees

I could just tell you to go practice Issue Trees.

But then this chapter wouldn’t exist!

Just kidding 🙂

Here’s the thing, telling people to go practice Issue Trees is what we did when we started our case interview coaching practice.

But it didn’t really work.

Most people would just memorize the common profit trees you see out there and try to apply them to different problems. The problem with that is that they weren’t building their ability to create new trees for new problems.

Other people would feel stuck. They’d get bogged down into the details and be afraid to do it wrong and waste their time. Or they wouldn’t know where to start.

So what did we do?

Over time we created different techniques for people to practice trees. Each one has a different function and they’re synergistic – the more techniques you use, the more you’ll learn.

Here are my four favorite ones:

As you can see there is a logic for the four types of practice I will suggest. (And yes, as a former consultant I can’t get over with 2×2 matrices.)

Case-specific practice is important because this type of practice is very targeted to what you’ll find in your case interviews.

But you also need more generic day-to-day practice because that will train your mind to always think in a structured way . Even when you’re in the bus. Even when you’re hanging out with your family. Even when the interviewer asks you that informal question about the time where you studied abroad.

On the vertical axis, you’ll find the type of problem you will be practicing with.

You need to practice with real problems you’ve tried to solve before because you are (or were) emotionally invested in them. You know nuances about them that you wouldn’t know about a random problem and you care (or have cared) about solving them. That gives you the rigor and confidence to structure problems with all the nuances and details they need.

But you also need to practice with hypothetical problems , problems you’ve never considered before. Why? Because that gives you the flexibility and confidence to structure any problem, even those you have never seen before!

It helps you be more creative and trains you to face the unknown. What’s the point of learning to structure problems if you can’t face new problems, after all?

Using the four techniques I’ll show you, you will get all four types of practice.

Actually, because this is a 2×2 matrix, practicing with three of these techniques should be enough to get you really good at this, so if you don’t like any of these, feel free to skip one of them if you want.

Practice #1: Creating "deep trees"

The first type of practice is that of creating very deep Issue Trees for hypothetical problems, simulating one you would do in a case interview if you had 20-30 minutes to think or one you would do in a real project.

The process is rather simple:

(1) Think of a problem (business or public sector) that someone might have to solve. It could be a WHY problem or a HOW problem.

(2) Create a multilayered Issue Tree to solve the problem. Aim for at least 6 layers and try to create even more than that as you get more practice.

What you’ll notice is that the first few layers are going to be quite easy, especially if the problem you chose to structure is a common one.

However, as you go deeper you’ll find that it gets harder and harder.

Because when you get deep into your Issue Tree you must deal with much more specific problems, problems that you might have never considered in your life before.

The deepest layers are the ones that teach you the most.

Everyone knows how to break down “profits” in a MECE way. Few people can break down “improving customer retention” in a MECE way. Even fewer can find a MECE structure on how to increase customer friction to leave to a competitor.

This exercise works wonders because most cases start really broad but they eventually get to really specific issues, such as “increasing customer friction to leave”, “outsourcing job tasks”, “reducing perceived purchase risks” and things like that.

Here’s an example of a “deep tree” for the “How to reduce costs in a widget manufacturing plant?” problem:

Hey, I’m the first to say this tree isn’t perfect, especially in the last couple layers. It’s really hard to create MECE structures to “buying terms and conditions” and other specific things like that.

And I only covered the “material costs” part, otherwise it wouldn’t fit the screen.

But I wanted to show you one example just do you could see how deep you should go when doing this kind of practice.

Practice #2: Restructuring past cases

Remember the last case you did? The one you messed up on the initial structure?

How much better would your structure be if you had 20-30 minutes to do it?

There’s a simple way to find out…

Restructure that case with as much time as you want!

This is a really good way to practice Issue Trees because (1) you internalize what you’ve learned in the case and (2) you can structure it with unlimited time and without being nervous.

Plus, let’s be honest, you keep telling yourself that your structures aren’t as good as they could be because you don’t have a lot of time to build them and you’re nervous.

But is that really the case?

Try it out!

This practice is as simple as the name suggests, but there is ONE NUANCE…

You will feel tempted to overemphasize the parts of the case your interviewer directed you to and underemphasize other areas.

So, for example, if you had a profitability case and the case ended up being about cutting labor costs in a telecom company, you will tend to make your structure much more robust in the labor costs part than in the rest of the tree.

DON’T DO THAT.

Instead, build a robust tree all around.

Maybe this case was about labor costs, but the next one could be on infrastructure costs and the one after that could be on pricing. Build a robust structure all around that simulates what you would’ve done had the interview gone in any of those directions.

Be prepared for every situation.

Practice #3: Solving real work problems

Got a problem at work?

Work like a consultant and build an Issue Tree first and foremost!

Have to hit a certain target in an organization you work at or collaborate with?

Break that metric down into an Issue Tree and find the best lever to focus on.

Have a school assignment?

Try to build an Issue Tree for it.

By doing these things you will incorporate Issue Trees in your daily work and study.

Sometimes I even create them as I read a book to better organize its ideas. And as I do that, I end up with the whole structure and all the important ideas of a book in just one page.

Practice #4: Creating "mental trees"

Remember I said you can do 3 out of the 4 types of practices in this chapter and still do fine?

Well, don’t skip this one.

Mental trees exercise a different muscle than the other practices, because it happens all in your head.

It’s kind of like mental math but for Issue Trees.

And it’s a skill that every consultant can do , and so should you.

So what are “mental trees”?

It’s simple. As you go through your day you will notice things. You will be curious about things. You will wonder how to fix certain problems or why they happen in the first place.

You’ll have questions such as:

“How could this restaurant generate more demand?”
“What could the city do to improve its transport system?”
“Why is the doctor always late for the appointment?”
“What will TV networks do to generate more revenue now that everyone’s on Youtube and Instagram?”

And as you have these questions, use these opportunities to create Issue Trees in your head.

Not huge ones, 2 or 3 layers is fine.

But do that and try to keep them in your head as you generate hypotheses for each bucket. At first this is gonna be really hard, but once you get the hang of it it will be a breeze.

And once it’s easy, you’ll be able to use Issue Trees whenever you need them.

This practice is especially important for final rounds because partners will often tell you to discuss a problem without using paper. (And they do expect you to structure it).

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Applying issue trees on the job.

If you’ve read this far, you’ve learned how to use the most versatile tool in solving business (and many other) problems.

And if you’re like me, you want to now maximize the value you got from learning this!

Issue Trees can help you be a better problem solver, but also to present your ideas better, to bring more and better insights and even to be a better manager.

In this chapter I’ll show you 5 direct, on-the-job applications of Issue Trees that you can use if you’re a consultant, if you work in industry and even if you have started your own business.

Issue Trees can be used in every facet of your job

Before we even jump into examples of direct applications of how to use Issue Trees on the job, let me make a bold claim: Issue Trees can be used in every facet of your job.

You know that saying about how everything looks like a nail to the guy who has a hammer?

Well, don’t think of Issue Trees as hammers.

They’re more like Swiss army knives or Microsoft Excel. It’s a tool with many functions.

And you can use it as a consultant, but also as an executive, as an entrepreneur and more. I once taught my dad who is a doctor how to use it and he’s now better able to explain his thought process and diagnostics to his patients.

Why am I telling you all this?

Just so you know that the 5 on-the-job applications I’m about to show you are some of the things you can do with Issue Trees.

With a bit of creativity you can do much more.

Application #1 - As a map to solve a specific problem

If you’ve spent any time at all as a knowledge worker in your career (that’s most analyst and management positions at most companies), you know how it feels to be stuck with a problem.

Most business problems start with a very simple, almost trivial, question, but as you dig deeper you start seeing all the nuances you feel overwhelmed.

It’s very different from the experience of solving a problem in business school, where all the information you’ll need (and all the info you’ll get) is in a neat 10-20 page case.

Anyway… When you feel overwhelmed, when you feel like there’s too much nuance to handle and when you feel like there’s so many directions to go what you need is a map. A high-level view of the problem with its distinct parts laid out in front of you so you can put numbers, hypotheses and plans to act in each part.

What you need is an Issue Tree.

Years ago I worked in a Venture Capital firm here in Brazil. They had just entered the market and wanted to invest in e-commerce.

My task was to figure out what types of e-commerce businesses would thrive in the country so they could invest well. Would it be auto-parts? Maybe fashion? Or perhaps food delivery?

It was an overwhelming task for me. There’s so many things you can do with e-commerce.

So what I did was to build two Issue Trees. One with our options and another with the high level criteria I’d want to see in each option for it to become a successful e-commerce.

Something like this:

Now, the real trees I did were a bit more sophisticated than this. They had:

More layers and a more MECE structure for the verticals
Other criteria for success not shown here
Prioritization so we could find the most important information first and eliminate whole verticals quickly

But you can get the idea… I got both of these trees and put into a spreadsheet and now I had a map of the problem that I could work on.

Because I used Issue Trees to create this map, I assured that the thinking was clear and rigorous, that I would be able to work efficiently by eliminating bad options quickly and that I’d bring insight to the table.

It also removed all overwhelm and made my work much more efficient. I no longer had to consider all the factors at once in my head. All I had to do now was to fill out a table with the best information I could get and see the results.

Application #2 - As a guide to brainstorm solutions

Brainstorming solutions to common business problems is a nervous activity.

Everyone wants to show the best solution, and people want to show common sense AS WELL AS creativity. It’s a tough spot to be in.

On top of that, people typically brainstorm solutions to problems that are urgent and critical (why fix what’s not broken?) and this is usually done in meetings, which adds to the pressure.

But that’s not all… In most meetings, solution generation happens in a haphazard way – completely different ideas are mentioned in the spam of a few minutes and it’s hard to even evaluate which are the best ones.

The result? The best solutions rarely win and it’s common that people don’t even reach a consensus on which should be implemented.

So, what’s the antidote?

You guessed it: Issue Trees.

If you have a solution generating meeting (or if you’re doing it by yourself) and you can find a HOW tree that reaches consensus (not actual solutions, but the structure of the problem) at the beginning of the meeting, you can then lead the discussion forward, helping people generate solutions for each bucket of your tree and then prioritizing those in an organized fashion.

Also, doing it this way tends to bring out more, better ideas – for the same reason why dividing the problem brings more creativity in case interviews. It’s easier to get 5 ideas per bucket than 40 for the problem as a whole.

I’ve been to both kinds of solution-generating meetings. One feels like a pointless chaos and the other gives you certainty that the problem will be solved from minute one.

Application #3 - As a way to structure a presentation

Structuring a presentation is the kind of thing that gets most people CRAZY.

You have to consider your audience, how to capture and keep attention, storytelling, getting your point across quickly and being to the point and so many other conflicting goals.

But here’s a simple way to do it: use the Issue Tree of the problem as a basis to how your presentation is organized.

This works because your Issue Tree is a map of your problem. And maps are great ways to make people understand a complex thing with simplicity and accuracy.

Let me show you an example of how to do this…

Remember the Telco executive from Chapter 1 that had a problem because his customers were unsubscribing from their services? I’ll help you remember it, it’s been a while…

Now, imagine he had to present what’s happening to the executive committee. It needed to be a short and to the point presentation that was compelling as well.

Not a full solution to the problem, but a presentation showing what happened.

What would you do in his place?

Here’s what I’d do:

Slide 1: A chart showing the high level problem (overall unsubscriptions have raised from 10.5 to 17 thousand clients, with an increase of 2,000 from clients willing to unsubscribe and 4,500 from clients being forced out).

I’d also add something that pointed out that the cause of the clients being forced out (the main problem) was a problem in the systems.

In other words, Slide 1 would be “High-level view” + “root-cause of main problem”. Everything the committee needs to understand the situation.

Slide 2: A chart detailing the root-cause of the main problem, with all details needed to understand why it happened. This would include numbers and qualitative things about that system problem.

Slide 3: A chart showing that even though we only lost 2,000 extra customers because they wanted out, we actually lost 3,000 to competition. I’d show the numbers (2nd Layer at “They wanted to unsubscribe” bucket) and show that there is potential there.

Slide 4: I’d turn back to the system’s problems and start talking about solutions. I’d show what was done, what is being done and what’s next to prevent it from happening again.

Slide 5: I’d show next steps to understand how to retain more customers vs. competition. This is a less urgent problem so I’d leave it at that.

That’s it, simple and straightforward.

And it all comes because I have a simple and straightforward Issue Tree that helps me solve and explain the problem in simple and straightforward ways.

Application #4 - As a guide to research best practices

We’ve all had that hurried boss that passes through your desk and casually mentions: “Hey, you should try to find some best practices around X”.

X can be anything he or she is concerned about: doing better presentations, sharing internal documents, improving productivity at work, getting more clients.

And the problem with that is that it’s really really hard to research that. If you just type “best practices for X” in google, chances are you’ll get some really generic, obvious tips.

One thing I’ve learned to do at McKinsey was to research best practices for each component of X. So instead of looking for best practices around “getting more clients”, I could research best practices to “get more leads” and “increasing conversion rate”.

And then I could break down those components even further and look for best practices for each sub-component.

Guess what’s the tool you need to get all the components in a logical manner? Yes, Issue Trees!

A normal best practice for X’s sub-sub-component usually is a great insight to improve X, so by simply doing this exercise you will come off way ahead of your peers as the go-to person for insights on how to improve your company.

Application #5 - As a way to generate KPIs and indicators

In case you don’t know the lingo, KPIs are you “Key Performance Indicators”.

They’re a business’ dashboard. The numbers you have to look at to see how healthy your business is.

But how do you create KPIs?

Well, in three simple steps:

1) You define your goals

2) Your break down your goals into the sub-components that must be true for you to achieve them

3) You figure out indicators for each prioritized sub-component. (Without the “prioritized” part, these indicators wouldn’t be “Key”)

So for example, if you’re studying for consulting interviews and you want to see how your preparation is going , here’s an example of how to create KPIs you can track:

Each bullet point could be a KPI. Some of these are numbers to track, others are Yes/No KPIs.

I am not saying nor implying every candidate should use all these KPIs to prepare, but notice how nuanced you can get when you use a MECE Issue Tree to create KPIs.

Most candidates just track the # of cases they did, without even caring for the quality of those.

No wonder why most get rejected.

It’s like a company that just tracks how many products it has sold without concerning about margins, customer retention rates, customer satisfaction, quality control and so on.

You can get any Issue Tree from this article and transform it into a list of KPIs to track within each important bucket.

There’s certainly an art on which ones are better to track (because you don’t want to end up with 35 different KPIs) but just generating them out of a MECE Issue Tree allows you to have at least one indicator to every important part of the problem, leaving no blind spots in your master dashboard.

What's next?

Issue Trees are one, but not the only tool MBB consultants use to solve their client’s problems.

There are actually 6 types of questions interviewers ask in case interviews, to test on the 6 most important tasks consultants perform in real client work.

You can learn about those questions and the specific tools, techniques and strategies management consultants from McKinsey, BCG and Bain use to solve business problems by joining our free course on case interviews!

By joining our course, you’ll get access to:

Step-by-step methods to solve the 6 (and only six) types of questions you can get in case interviews
The “Landscape Technique” to create conceptual frameworks from scratch (this is the technique you need when Issue Trees fail to help you)
Tons of practice drills so you can apply your knowledge

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Issue Trees – What Are They and How Do You Use Them?

Issue trees are a useful approach to breaking down a problem statement into component parts that can more easily be acted upon. In consulting teams, it’s often done in the first couple of weeks of a project. It enables the team to structure the project in a way that people can be assigned to specific “workstreams” and that the team can align their hypotheses to make predictions about which elements might have the biggest impact.

I like the definition that McKinsey Mind uses for issue trees:

The issue tree, a species of logic tree in which each branch of the tree is an issue or question, bridges the gap between structure and hypothesis. Every issue generated by a framework will likely be reducible to sub issues, and these in turn may break down further. An issue tree is simply the laying out of issues and subissues into a MECE visual progression. By answering the questions in the issue tree, you can very quickly determine the validity of your hypothesis.

It’s a good definition but it’s also chock-full of jargon and we’re not a fan of jargon (or at least egregious uses of it) here are StrategyU The simplest way of thinking about an issue tree is as a way of breaking down a complex problem into many possible explanations of what is going wrong.

What Do They Look Like And How Do You Use Them ?

Issue trees are often created visually in PowerPoint but can also be in the form of financial models. The type of issue tree we are concerned about are the ones that help us structure our central problem or mission. For example, most companies are focused on increasing profitability. We might frame this “problem” in the form of a question, “Company profitability is declining, what are the ways to improve it?”

We can then start to brainstorm different ways that profitability might be increased. At the higher levels, you want to be as broad as possible such that you can break the tree down further and get more specific the deeper you go. You’ll also want to try to use MECE . Our initial issue tree might look like this:

From there we can go deeper. What are the different ways we can increase revenue. It’s best to just start listing ideas and then start thinking about how to synthesize them, organize them, and yes, make sure they are MECE!

You might develop the next leg of your tree:

This tree is not perfect and the answers at the lowest level are not collectively exhaustive for all the possibilities for increasing revenue and decreasing costs. However, for a specific company, these may be the relevant issues, meaning that they are the ones that you are able to invest money on, tweak and that might have a positive impact.

The next step is to develop analyses or experiments that you can perform to validate or quantify how much impact can be generated by focusing in each of these areas

How Issue Trees Are Linked With Problem Solving

At StrategyU we are fans of the SCQA process to define problems and develop hypotheses. This approach enables us to have a rigorous problem-solving approach to business problems instead of starting with the solution in mind from the beginning. This approach works best when you are open-minded and flexible. The first test of the issue tree is when you are doing the initial research and analysis after you structure the problem. This is step two of the consulting process :

bottom-up sensemaking in strategy consulting and pyramid principle

At this point, you will likely get some quick feedback on your initial problem statement such as:

Have we defined the problem appropriately or are there deeper issues?
Have we identified the relevant issues and areas in which we can make a difference?
What kind of initial tests have we done are are we designing to confirm if the issues and questions are right?

This is a frustrating, iterative process and within a consulting team, you are often revisiting the issue tree and problem statement over and over again throughout a project.

How To Use This In Your Company

You should have a good understanding of the “levers” that help your company continue to grow, increase its profitability, and improve over time. Spend long enough in any company and you start to realize that there are a narrow set of metrics everyone makes decisions around. Except unless you’ve mapped this out explicitly, there will likely be many different definitions and interpretations of what you are optimizing for.

Using a template like follows and coming up with the high-level issues and areas within the business you are focusing on can be clarifying. You can also add specific types of analyses and information that you use to help you solve or improve in these areas:

This can also be rolled out across your org chart. Let’s imagine a company realizes that it doesn’t have much room to lower costs anymore and it wants to focus exclusively on increasing revenue. They can do this in two ways (assuming they aren’t adding new products). They can increase the price per order or they can increase volume. They may when want to break this down into different sub-issues.

In reality, you’d want to collect a lot of data and verify that the way you are breaking things down is correct. The numbers often surprise companies. They realize that an area of focus (increasing # of customers, for example) is not as big of an impact on the bottom line as other areas.

The only way to figure this out is to map out all of the possibilities of your issues and then validate them with real data.

This is the same thing that consulting teams do when they work for companies.

In my course, Think Like A Strategy Consultant, you have to complete an issue tree for a case example featuring Facebook’s transition from desktop to mobile and I’ll walk you through the process step-by-step which also providing you feedback if you want. Learn more here .

Do you have a toolkit for business problem solving? I created Think Like a Strategy Consultant as an online course to make the tools of strategy consultants accessible to driven professionals, executives, and consultants. This course teaches you how to synthesize information into compelling insights, structure your information in ways that help you solve problems, and develop presentations that resonate at the C-Level. Click here to learn more or if you are interested in getting started now, enroll in the self-paced version ($497) or hands-on coaching version ($997). Both versions include lifetime access and all future updates.

Consulting Hypothesis Tree: Everything You Need to Know

Last Updated June, 2023

A hypothesis tree is a powerful problem-solving framework used by consultants. It takes your hypothesis, your best guess at the solution to your client’s problem, and breaks it down into smaller parts to prove or disprove. With a hypothesis tree, you can focus on what’s important without getting bogged down in details.

Are you feeling overwhelmed during a complex case interview? Try using a hypothesis tree! It’ll help you communicate your insights more effectively, increasing your chances of acing the case.

In this article, we’ll discuss:

What a hypothesis tree is, and why it’s important in consulting interviews
Differences between a hypothesis tree vs. an issue tree
The structure of a hypothesis tree and how to construct one
A hypothesis tree example
Our 4 tips for using a hypothesis tree effectively in consulting interviews

Let’s get started!

Definition of a Hypothesis Tree and Why It's Important

6 steps to build a hypothesis tree, hypothesis tree example, 4 tips for using a hypothesis tree in your interview, limitations to using a hypothesis tree, other consulting concepts related to hypothesis trees.

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A hypothesis tree is a tool consultants use to tackle complex problems by organizing potential insights around a central hypothesis. It provides a structured framework for solving problems by forming sub-hypotheses that, if true, support the central hypothesis. This allows consultants to explore problems more effectively and communicate their insights.

Mastering the hypothesis tree can help you stand out in your case interview. It enables you to showcase your problem-solving skills and critical thinking ability by presenting insights and hypotheses in a concise and organized manner. This helps you avoid getting overwhelmed by the complexity of the client’s problem.

Hypothesis trees are not limited to consulting interviews; they are an essential tool in real-world consulting projects! At the beginning of a project, the partner in charge or the manager will create a hypothesis tree to scope the problem, identify potential solutions, and assign project roles. Acting as a “north star,” a hypothesis tree gives a clear direction for the team, aligning their efforts toward solving the problem. Throughout the project, the team can adapt and refine the hypothesis tree as new information emerges.

The terms “hypothesis tree” and “issue tree” are often used interchangeably in consulting. However, it’s important to understand their key differences.

Differences Between a Hypothesis Tree vs. an Issue Tree

A hypothesis tree is less flexible as it is based on a predetermined hypothesis or set of hypotheses. In contrast, an issue tree can be more flexible in its approach to breaking down a problem and identifying potential solutions.

Let’s look at a client problem and high-level solution frameworks to illustrate the differences: TelCo wants to expand to a new geography. How can we help our client determine their market entry strategy?

If you were to start building a hypothesis tree to explore this, your hypothesis tree might include:

Hypothesis: TelCo should enter the new market.

It has immense potential and is growing rapidly.
The expansion is forecasted to be profitable as the costs to operate the service in the new market are low.
There are few large competitors, and our product has a competitive advantage.
How attractive is the new market? What is the growth outlook? What is the profitability forecast for this new market?
What are the different customer segments?
How is our client’s service differentiated from local competitors?

Nail the case & fit interview with strategies from former MBB Interviewers that have helped 89.6% of our clients pass the case interview.

Here are the 6 steps to build a hypothesis tree. Practice doing these in your mock case interviews!

1. Understand the Problem

Before building a hypothesis tree, you need to understand the problem thoroughly. Gather all the information and data related to the problem. In a case interview, ask clarifying questions after the interviewer has delivered the case problem to help you build a better hypothesis.

2. Brainstorm

Brainstorm and generate as many hypotheses as possible that could solve the problem. Ensure that the hypotheses are MECE. In your interview, you can ask for a few moments to write down your brainstorming before communicating them in a structured way.

3. Organize the Hypotheses

Once you have brainstormed, organize your thoughts into a structured hierarchy. Each hypothesis should be represented as a separate branch in the hierarchy, with supporting hypotheses below.

4. Evaluate the Hypotheses

Evaluate each hypothesis based on its feasibility, relevance, and potential impact on the problem. Eliminate any hypotheses that are unlikely to be valid or don’t provide significant value to the analysis. During your interview, focus on the highest likelihood solutions first. You will not have the time to go through all your hypotheses.

5. Test the Hypotheses

Test your central hypothesis by confirming or refuting each of the sub-hypotheses. If you need data to do this, ask your interviewer for it. Analyze any information you receive and interpret its impact on your hypothesis before moving on. Does it confirm or refute it?

If it refutes your hypothesis, don’t worry. That doesn’t mean you’ve botched your case interview. You just need to pivot to a new hypothesis based on this information.

6. Refine the Hypotheses

Refine the hypothesis tree as you learn more from data or exhibits. You might need to adjust your hypothesis or the structure of the hypothesis tree based on what you learn.

Let’s go back to the TelCo market entry example from earlier.

Hypothesis : TelCo should enter the Indian market and provide internet service.

Market Opportunity : The Indian market is attractive to TelCo.

The Indian telecommunications market is growing rapidly, and there is room for another provider.
Margins are higher than in TelCo’s other markets.
The target customer segments are urban and rural areas with high population densities.
The competition is low, and there is an opportunity for a new provider for customers who need reliable and affordable service.

Operational Capabilities : The company has the capacity and resources to operate in India.

TelCo can leverage its existing expertise and technology to gain a competitive advantage.
TelCo should build out its Indian operations to minimize costs and maximize efficiency.
TelCo should consider investing in existing local infrastructure to ensure reliable service delivery.
TelCo can explore alliances with technology content providers to offer value-added services to customers.

Regulatory Environment : The local regulators approve of a new provider entering the market.

TelCo must ensure compliance with Indian telecommunications regulations.
TelCo should also be aware of any upcoming regulatory changes that may impact its business operations.

Overall, this hypothesis tree can help guide the analysis and process to conclude if TelCo should enter the Indian market.

1. Develop Common Industry Knowledge

By familiarizing yourself with common industry problems and solutions, you can build a foundation of high-level industry knowledge to help you form relevant hypotheses during your case interviews.

For example, in the mining industry, problems often revolve around declining profitability and extraction quality. Solutions may include reducing waste, optimizing resources, and exploring new sites.

In retail banking, declining customer satisfaction and retention are common problems. Potential solutions are improving customer service, simplifying communication, and optimizing digital solutions.

Consulting club case books like this one from the Fuqua School of Business frequently have industry overviews you can refer to.

2. Practice Building Hypothesis Trees

Building a hypothesis tree requires practice. Look for opportunities to practice generating hypotheses in everyday situations, such as when reading news articles or listening to podcasts. This will help you develop your ability to structure your thoughts and ideas quickly and naturally.

3. Use Frameworks to Guide Building a Hypothesis Tree

Remember, you can reference common business frameworks, such as the profitability formula, as inputs to your hypothesis. Use frameworks as a starting point, but don’t be afraid to deviate from them if it leads to a better hypothesis tree.

Interviewers expect candidates to tailor their approach to the specific client situation. Try to think outside the box and consider new perspectives that may not fit neatly into a framework.

For an overview of common concepts, we have an article on Case Interview Frameworks .

4. Embrace Flexibility

Don’t be afraid to pivot your hypotheses and adjust your approach based on new data or insights. This demonstrates professionalism and openness to feedback, which are highly valued traits in consulting.

Although hypothesis trees are a helpful tool for problem-solving, they have limitations.

The team’s expertise and understanding of the problem are crucial to generating a complete and accurate hypothesis. Relying on a hypothesis tree poses the risk of confirmation bias, as the team may unconsciously favor a solution based on past experiences. This is particularly risky in rapidly evolving industries, such as healthcare technology, where solutions that have worked for past clients may no longer be relevant due to regulatory changes.

A hypothesis tree can also be inflexible in incorporating new information mid-project. It may accidentally limit creativity if teams potentially overlook alternative solutions.

It’s important to be aware of these limitations and use a hypothesis tree with other problem-solving methods.

Several concepts in consulting are related to hypothesis trees. They all provide a structure for problem-solving and analysis. Each has its unique strengths and applications, and consultants may use a combination of these concepts depending on the specific needs of the problem.

Let’s look at some concepts:

Issue Trees : As mentioned earlier in the article, issue trees are similar to hypothesis trees, but instead of starting with a hypothesis, they start with a problem and break it down into smaller, more manageable issues. Issue trees are often used to identify a problem’s root cause and to prioritize which sub-issues to focus on. If you want to learn more, we have a detailed explanation of Issue Trees .
MECE Structure : MECE stands for mutually exclusive, collectively exhaustive. It is used to organize information and ensure that all possible options are considered. It is often used in conjunction with a hypothesis tree to ensure that all potential hypotheses are considered and that there is no overlap in the analysis. For an overview of the MECE Case Structure , check out our article.
Pyramid Principle : This is a communication framework for structuring presentations, such as case interviews. It starts with a hypothesis and three to four key arguments, each with supporting evidence. You can use it throughout the case for structuring and communicating ideas, such as at the beginning of a case interview to synthesize your thoughts or when brainstorming ideas in a structured way. To better understand why this tool is valuable, we have a deep dive into The Pyramid Principle .
Hypothesis-Driven Approach : This is an approach to problem-solving where consultants begin by forming a hypothesis after understanding the client’s problem and high-level range of possibilities. Then, they gather data to test the initial hypothesis. If the data disproves the hypothesis, the consultants repeat the process with the next best hypothesis. To see more examples, read our article on how to apply a Hypothesis-Driven Approach .

– – – – – – –

In this article, we’ve covered:

Understanding the purpose of a hypothesis tree
What is different about a hypothesis tree vs. issue tree?
How to build a hypothesis tree
4 tips on how to successfully use a hypothesis tree in your consulting case interview
Other consulting concepts that are related to hypothesis trees

Still have questions?

If you have more questions about building a hypothesis tree, leave them in the comments below. One of My Consulting Offer’s case coaches will answer them.

Other people interested in the hypothesis tree found the following pages helpful:

Our Ultimate Guide to Case Interview Prep
Issue Trees
Hypothesis-Driven Approach
MECE Case Structure

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3 Top Strategies to Master the Case Interview in Under a Week

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MECE Framework McKinsey

“MECE,” pronounced “me see,” an acronym for “mutually exclusive and collectively exhaustive,” is a popular mantra at McKinsey. If you manage to get a position at McKinsey, or at any other MBB for that matter, you are likely to have to handle huge amounts of data. Chances are that your boss will ask you to ensure that you organize your data in an “MECE manner.” “Be more ‘MECE’ in your approach,” she might say. In fact, even at your case interview, you likely used the MECE principle. You might not have got the green signal if you hadn’t.

Introduction and definition

MECE is a method of grouping information into elements that are mutually exclusive (ME) and collectively exhaustive (CE). In other words, it is a process by which information—ideas, topics, issues, solutions—is arranged or, put in “MECE buckets,” with no overlapping between buckets and with each item having a place in one bucket only (ME), and with the buckets including all possible items relevant to the context.

A simple example of the MECE principle would be the classification of the population into age groups. Here, dividing the population into two groups, one group of people above, say, 60, and another group below 60, would be based on the MECE principle. The entire population would be either above or below 60 (ME, with no overlapping between the two buckets) and with all people included in one or the other bucket (CE).

However, a categorization of the population into one group of, say, people below 60 and another of people between 50 and 70 would not be based on MECE. People between 50 and 60 would be in both “buckets” (not ME) and some people would not be in either bucket (so not CE).

How is it used?

Strategy consultants use the MECE framework (Issue Tree, Decision Tree, Hypothesis Tree) to segregate a client’s problems into logical data categories that can be analyzed systematically and minutely by their staff involved with the project. The framework is notably used at McKinsey, where data from clients’ businesses is organized on the basis of MECE. Well-known frameworks, such as Cost-Benefit Analysis, 4Cs, and Porter’s Five Forces have the MECE principle at their core.

Let’s discuss three popular MECE frameworks.

How is the MECE framework used to solve clients’ issues at consultancies? One method consultants use is to create an “issue tree” to arrange all the information that they have and divide this information into all possible issues and sub-issues.

An issue tree is particularly helpful for solving large and complex problems as it facilitates splitting them up into smaller, solvable problems. “Issue trees” get their names from their structure—narrow at the top with the problem statement, and wider towards the bottom, even as each level accommodates more specific sub-issues or smaller problems. However, some “trees” are also created left to right, but the principle remains the same.

A common type of cases in which a MECE issue tree is used is profitability cases. Suppose the problem statement is “My restaurant is not profitable.” An issue tree is created, starting with the problem statement at the treetop.

The various sub-levels of the tree would answer the question “How to make the restaurant profitable?” in broad, intuitive ways: “Increase revenue” and “Reduce costs.” The lower levels would also answer the question “How?”

The second level, with sub-issues of the first level, would answer the questions “How to increase revenue?” (under “Increase revenue”) on the one hand and “How to reduce costs?” on the other. The answers under “Increase revenue” would be “Increase the number of orders” and “Increase the prices of items.” The answers under “Reduce costs” would be “Reduce salary expenditure,” “Reduce rental,” and “Reduce raw material expenses.”

On the third level, the issue tree would tackle the question “How to Increase the number of orders?” One way to increase orders would be to shift the restaurant to a busier area and another would be to launch a marketing campaign so that the restaurant becomes more widely known. On the other main branch, under “reduce salary expenditure,” options such as “fire redundant workers” could be mentioned, as also “shift to a less expensive locality” under “reduce rental”, and “change the vendors,” under “reduce raw material expenses.”

How does an issue tree help? It enables consultants to consider all options separately and exclusively and suggest the best option to the client. It helps create a common understanding among team members about the problem-solving framework and focus team efforts. It smoothens work distribution among team members.

Often, consultants who create an issue tree may need to “trim branches,” which means doing away with options that are not worth pursuing after a detailed initial consideration. In the example of the issue tree, given above, about how to increase the profitability of a restaurant, increasing prices may not be an option for various reasons, and that “branch” of the issue tree may be left out or “trimmed.”

Decision tree

A decision tree is a tree-shaped graphical representation of decisions and potential outcomes of those decisions, and is used to determine a course of action. A decision tree helps users understand the comparative advantages and disadvantages of each decision and outcome.

A decision tree is often drawn from left to right. It starts with a specific decision denoted by a small square. “Branches,” or lines, are drawn to the right from the square, representing each potential option. If the option is a new decision, a square is drawn, and from it, new branches are drawn, representing new options. At the end of each branch, a circle is drawn if the result of the option is unclear. If the option leads to a decision that helps bring about a solution, the branch is left blank. A triangle is also used to signify the end of a branch or path to a potential solution.

Like an issue tree, a decision tree is exhaustive in its inclusion of decision, outcomes, options, and scenarios. A user of a decision tree looks at each of them and chooses the best option.

Hypothesis tree

Another method to structure a problem is to develop a hypothesis tree, which is the graphical representation of all MECE hypothesis that elucidates the problem. It is, in a way, similar to an issue tree, where a problem is broken down into its components, which makes identifying and solving it easier. But while an issue tree splits up each problem into issues and sub-issues, a hypothesis tree organizes a problem around hypotheses, and often offers a more direct approach than an issue tree.

Developing MECE hypotheses

First, understand the problem thoroughly. What are you trying to solve?

Second, write down the problem statement. Take care to ensure clarity in the statement so that there is no ambiguity.

Third, list the options to solve the problem, using a MECE tree. See that the options do not overlap (that they are mutually exclusive) and that no option has been left out (that they are collectively exhaustive).

Fourth, consider each option individually. Consider the pros and cons. Leave out those that are illogical and include any new insight as an option as you understand the problem better.

Fifth, select the best option and present it to the client.

Clarity pays

Good management consultants use the MECE structure for problem-solving. A piece of advice they like to give to aspiring consultants is to learn to use the MECE principle for not only structuring problems but also communicating solutions, whether they are attending a case interview at MBB or sitting across the desk from a client.

Creating a MECE hypothesis helps clarifies a problem. It’s like having a road map when you are lost in unknown territory. If your approach to structuring a problem is “not MECE,” “it is probably messy,” as they say. In order to truly understand MECE, you’ll first need a solid foundation of business. Which is what we teach in our popular online course. It covers other important strategy concepts (including powerful problem solving frameworks), as well as the wider range of super-essential business topics. Check it out here Mini MBA

Resources: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14

Issue Tree in Consulting: A Complete Guide (With Examples)

What’s the secret to nailing every case interview ? Is it learning the so-called frameworks? Nuh-uh.

Actually, that secret lies in an under-appreciated, yet extremely powerful problem-solving tool behind every real consulting project . It’s called the “issue tree”, also known as “logic tree” or “hypothesis tree” – and this article will teach you how to master it.

Table of Contents

What is an issue tree?

An issue tree is a pyramidal breakdown of one problem into multiple levels of subsets, called “branches”. It can be presented vertically (top-to-bottom), or horizontally (left-to-right). An issue tree systematically isolates the root causes and ensures impactful solutions to the given problem.

The issue tree is most well-known in management consulting , where consultants use it within the “hypothesis-driven problem-solving approach” - repeatedly hypothesizing the location of the root causes within each branch and testing that hypothesis with data. Once all branches are covered and root causes are found, impactful solutions can be delivered.

The issue tree is only part of the process used in case interviews or consulting projects. As such, it must be learned within the larger context of consulting problem-solving, with six concepts: problem, root cause, issue tree, hypothesis, data & solution , that strictly follow the MECE principle .

Every problem-solving process starts with a well-defined PROBLEM...

A problem is “well-defined” when it is attached with an objective. Let’s get straight to a business problem so you can get a good perspective on how it is done. So here’s one:

Harley-Davidson, a motorcycle company, is suffering from negative profit. Find out why and present a solution.

Now we’ve got our first piece of the tree:

...then tries to find its ROOT CAUSES…

To ensure any solution to the problem is long-lasting, consultants always look for the root cause.

Problems are often the last, visible part in a long chain of causes and consequences. Consultants must identify the very start of that chain – the root cause – and promptly deal with it to ensure that the problem is gone for good.

The diagram is a simple representation. Real problems can have multiple root causes. That’s where the issue tree comes to the rescue.

Since Harley has been reporting losses, it tried to decrease cost (in the simplest sense, profit = revenue - cost) by shutting down ineffective stores. As you may have imagined, it wasn’t very effective, so Harley set out to find the real source of the problem.

...by breaking it down into different BRANCHES of an issue tree

An issue tree ensures that all root causes are identified in a structured manner by breaking the problem down to different “branches”; each branch is in turn broken down into contributing sub-factors or sub-branches. This process is repeated through many levels until the root causes are isolated and identified.

For this problem, Harley deducted that losses must be due to decreasing revenue or increasing cost. Each branch is in turn segmented based on the possible reasons

For a branch to be included in the issue tree, there must be a possibility that it leads to the problem (otherwise, your problem-solving efforts will be wasted on the irrelevant).

To ensure that all possibilities are covered in the issue tree in a neatly organized fashion, consultants use a principle called “ MECE ”. We’ll get into MECE a bit later.

A HYPOTHESIS is made with each branch…

After we’ve developed a few branches for our issue tree, it’s time to hypothesize, or make an educated guess on which branch is the most likely to contain a root cause.

Hypotheses must adhere to 3 criteria:

It must follow the issue tree – you cannot hypothesize on anything outside the tree

It must be top-down – you must always start with the first level of the issue tree

It must be based on existing information – if your information suggests that the root cause is in branch A, you cannot hypothesize that the root cause comes from branch B

Once a hypothesis is confirmed as true (the root cause is inside that branch), move down the branch with a lower-level hypothesis; otherwise, eliminate that branch and move sideways to another one on the same level.

Repeat this process until the whole issue tree is covered and all root causes are identified.

Harley hypothesized lower revenue is either due to losing its customers because they came to competitors or they weren’t buying anymore, or it couldn’t attract new buyers

But wait! A little reminder: When solving an issue tree, many make the mistake of skipping levels, ASSUMING that the hypothesis is true instead of CONFIRMING it is.

So, in our example, that means from negative profit, we go straight into “losing old customers” or “can’t attract new customers” before confirming that “decreasing revenue” is true. So if you come back and reconfirm “decreasing revenue” is wrong, your case is completely off, and that’s not something consultants will appreciate, right?

Another common mistake is hopping between sub-branches before confirming or rejecting one branch , so that means you just jump around “losing old customers” and “can’t attract new customers” repeatedly, just to make haste of things. Take things very slowly, step-by-step. You have all the time in the world for your case interview.

But testing multiple sub-branches is possible, so long as they are all under the same branch and have the same assessment criteria.

So for our example, if you are assessing the sales of each motorcycle segment for Harley, you can test all of them at once.

The hypothesis is then tested with DATA...

A hypothesis must always be tested with data.

Data usually yield more insights with benchmarks – reference points for comparison. The two most common benchmarks in consulting are historical (past figures from the same entity) and competitor (figures from similar entities, in the same timeframe).

Using “competitor benchmark” to test if competitors are drawing away customers, Harley found that its competitors are also reporting losses, so it must be from something else!

...to find an ACTIONABLE SOLUTION

After the analyzing process, it’s time to deliver actionable solutions. The solutions must attack all the root causes to ensure long-lasting impact – if even one root cause remains untouched, the problem will persist.

Remember to deliver your solutions in a structured fashion, by organizing them in neat and meaningful categories; most of the time, solutions are classified into short-term and long-term.

So Harley found that it is losing its traditional customer base - old people, as they were the most vulnerable groups in the pandemic, so they stopped buying motorcycles to save money for essentials, or simply didn’t survive.

Harley also found that it can’t attract new, younger buyers, because of its “old-school” stigma, while also selling at premium price tags. So the short-term solution is setting more attractive prices to get more buyers; and the long-term solution is renewing itself to attract younger audiences.

Our case was a real problem for Harley-Davidson during the pandemic, whose sales plummeted because its target audience were either prioritizing essentials, or dead. So now, Harley has to change itself to attract younger people, or die with its former customer base.

What Is MECE and How Is It Used in an Issue Tree?

A proper issue tree must be MECE, or “ Mutually Exclusive, Collectively Exhaustive .” Mutually exclusive means there’s no overlap between the branches, and collectively exhaustive means all the branches cover every possibility. This is a standard all management consultants swear by, and together with the issue tree, a signature of the industry.

To answer whether an issue tree is MECE or not, you need to know all the basic and “advanced” rules of the MECE principle, and we’ll talk about those here. If you want a more comprehensive guide on MECE, check out our dedicated article on MECE .

Basic rule #1: Mutually exclusive

Adherence to this rule ensures that there will be no duplicated efforts, leading to maximum efficiency in problem-solving. It also allows the consultant to isolate the root cause more easily; otherwise, one root cause may manifest in multiple branches, making it harder to pinpoint.

For example, an apparel distributor trying to find out the cause of its decreasing unit sales may use the cleanly-separated product segments: High-end, mid-range, and entry-level. A non-mutually exclusive segmentation here would be: high-end products and footwear.

Basic rule #2: Collectively exhaustive

A collectively exhaustive issue tree also covers only the relevant factors - if one factor is not related to the problem, it must not be included.

If the aforementioned apparel distributor omits any of the product segments in its analysis, it may also ignore one or a few root causes, leading to ineffective problem-solving. But even if it produces runway-exclusive, not-for-sale pieces, those are not included in the issue tree because they don't contribute to unit sales.

Advanced rule #1: Parallel items

This rule requires that all items are on the same logical level.

High-end, mid-range, and entry-level are three parallel and MECE branches. But if we replace the first two with “high-and-mid-range”, the whole issue tree becomes non-parallel and non-MECE, because the new branch is one level higher than the remaining “entry-level” branch.

Advanced rule #2: Orderly List

This rule requires that all items are arranged in a logical order.

So for our apparel distributor, the branches can be arranged as high-mid-low or low-mid-high. Never go “high-low-mid” or “mid-low-high”, because this arrangement is illogical and counter-intuitive.

Advanced rule #3: The “Rule of Three”

The ideal number of branches on any level of the issue tree is three - the most intuitive number to the human mind.

Three items are often enough to yield significant insights, while still being easy to analyze and follow; segmentations into 2 or 4 are also common. 5 is acceptable, but anything more than that should be avoided.

Our apparel distributor may have dozens of product lines across the segments, but having that same number of branches in the issue tree is counter-intuitive and counter-productive, so we use the much more manageable 3 segments.

Advanced rule #4: No Interlinking Items

There should be minimal, and ideally no connections between the branches of the issue tree.

If the branches are interlinked, one root-cause may manifest itself in multiple symptoms across the tree, creating unnecessary confusion in the problem-solving process.

Variants of an issue tree

Beside the “why tree” we used to solve why Harley was reporting losses, there are two other common trees, the “which tree” and the “how tree.” The which tree answers which you should do among the choices, and the how tree answers how you should do something.

Why tree helps locate and attack root causes of a problem

We’ve shown you how a why tree could be used to break down a problem into smaller pieces to find the root causes, which involves several important concepts, but in short there are 3 things you need to do:

Locate root causes by narrowing down your search area. To quickly locate root causes, use breakdown by math, process, steps or segment, or any combination of those. We’ll talk about that a bit later

Identify root causes from what you’ve hypothesized. Remember, all hypotheses must be tested with data before reaching a conclusion

Suggest solutions to attack the root causes to eliminate the problem for good. However, sometimes the root causes cannot be solved effectively and efficiently, so we might also try to mitigate their effects

Which tree helps make the most suitable decision

The which tree is a decision-making table combining two separate issue trees – the available options, and the criteria. The options and criteria included must be relevant to the decision-maker. When considering choosing X over something, consultants might take a look at several factors:

Direct benefits: Does X generate more key output on its own?

Indirect benefits: Does X interact with other processes in a way that generates more key output?

Costs: What are the additional costs that X incur?

Risks: Can we accept the risks of either losing some benefits or increasing cost beyond our control?

Feasibility: Do we have enough resources and capability to do X?

Alternative: Are there any other alternatives that are better-suited to our interests?

Additionally, the issue tree in “Should I Do A or B” cases only contains one level. This allows you to focus on the most suitable options (by filtering out the less relevant), ensuring a top-down, efficient decision-making process.

How tree helps realize an objective

The how tree breaks down possible courses of action to reach an objective. The branches of the tree represent ideas, steps, or aspects of the work. A basic framework for a how tree may look like this:

Identify steps necessary to realize the objective

Identify options for each steps

Choose the best options after evaluations

Again, like the two previous types of issue trees, the ideas/steps/work aspects included must be relevant to the task.

A restaurant business looking to increase its profitability may look into the following ideas:

Consulting frameworks – templates for issue trees

Don’t believe in frameworks….

In management consulting, frameworks are convenient templates used to break down and solve business problems (i.e. drawing issue trees).

So you might have heard of some very specific frameworks such as the 4P/7P, or the 3C&P or whatever. But no 2 cases are the same, and the moment you get too reliant on a specific framework is when you realize that you’re stuck.

The truth is, there is no truly “good” framework you can use. Everyone knows how to recite frameworks, so really you aren’t impressing anyone.

The best frameworks are the simplest, easiest to use , but still help you dig out the root causes.

“Simplest, easiest to use” also means you can flexibly combine frameworks to solve any cases, instead of scrambling with the P’s and the C’s, whatever they mean.

“Simplest, easiest to use” frameworks for your case interviews

There are 5 ways you can break down a problem, either through math, segments, steps, opposing sides or stakeholders.

Math : This one is pretty straightforward, you break a problem down using equations and formulae. This breakdown easily ensures MECE and the causes are easily identified, but is shallow, and cannot guarantee the root causes are isolated. An example of this is breaking down profits = revenues - costs

Segments : You break a whole problem down to smaller segments (duh!). For example, one company may break down its US markets into the Northeast, Midwest, South and West regions and start looking at each region to find the problems

Steps : You break a problem down to smaller steps on how to address it. For example, a furniture company finds that customers are reporting faulty products, it may look into the process (or steps) on how its products are made, and find the problems within each steps

Opposing sides : You break a problem down to opposing/parallel sides. An example of this is to break down the solution into short-term and long-term

Stakeholders: You break a problem down into different interacting factors, such as the company itself, customers, competitors, products, etc.

To comprehend the issue tree in greater detail, check out our video and youtube channel :

Scoring in the McKinsey PSG/Digital Assessment

The scoring mechanism in the McKinsey Digital Assessment

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A case interview is where candidates is asked to solve a business problem. They are used by consulting firms to evaluate problem-solving skill & soft skills

Case interview frameworks are methods for addressing and solving business cases. A framework can be extensively customized or off-the-shelf for specific cases.

MECE is a useful problem-solving principle for case interview frameworks with 2 parts: no overlap between pieces & all pieces combined form the original item

Hacking the Case Interview

An issue tree is a structured framework used to break down and analyze complex problems or questions into smaller components. It is a visual representation of the various aspects, sub-issues, and potential solutions related to a particular problem.

Issue trees are commonly used in business, consulting, problem-solving, and decision-making processes.

If you’re looking to better understand issue trees and how to use them in consulting case interviews or in business, we have you covered.

In this comprehensive article, we’ll cover:

What is an issue tree?
Why are issue trees important?
How do I create an issue tree?
How do I use issue trees in consulting case interviews?
What are examples of issue trees?
What are tips for making effective issue trees?

If you’re looking for a step-by-step shortcut to learn case interviews quickly, enroll in our case interview course . These insider strategies from a former Bain interviewer helped 30,000+ land consulting offers while saving hundreds of hours of prep time.

What is an Issue Tree?

An issue tree is a visual representation of a complex problem or question broken down into smaller, more manageable components. It consists of a top level issue, visualized as the root question, and sub-issues, visualized as branches and sub-branches.

Top Level Issue (Root Question) : This is the main problem or question that needs to be addressed. It forms the root of the tree.
Sub-issues (Branches) : Underneath the top level issue are branches representing the major categories or dimensions of the problem. These are the high-level areas that contribute to the overall problem.
Further Sub-issues (Sub-branches) : Each branch can be broken down further into more specific sub-issues.

Issue trees generally take on the following structure.

Issue trees get their name because the primary issue that you are solving for can be broken down into smaller issues or branches. These issues can then be further broken down into even smaller issues or branches.

This can be continued until you are left with a long list of small issues that are much simpler and more manageable. No matter how complicated or difficult a problem is, an issue tree can provide a way to structure the problem to make it easier to solve.

As an example, let’s say that we are trying to help a lemonade stand increase their profits. The overall problem is determining how to increase profits.

Since profits is equal to revenue minus costs, we can break this problem down into two smaller problems:

How can we increase revenues?
How can we decrease costs?

Since revenue is equal to quantity times price, we can further break this revenue problem down into two even smaller problems:

How can we increase quantity sold?
How can we increase price?

Looking at the problem of how to increase quantity sold, we can further break that problem down:

How can we increase the quantity of lemonade sold?
How can we increase the quantity of other goods sold?

We can repeat the same procedure for the costs problem since we know that costs equal variable costs plus fixed costs.

How can we decrease variable costs?
How can we decrease fixed costs?

Looking at the problem of how to decrease variable costs, we can further break that down by the different variable cost components of lemonade:

How can we decrease costs of lemons?
How can we decrease costs of water?
How can we decrease costs of ice?
How can we decrease costs of sugar?
How can we decrease costs of cups?

The overall issue tree for this example would look like the following:

In this example, the issue tree is a special kind of issue tree known as a profit tree.

Why are Issue Trees Important?

Issue trees are helpful because they facilitate systematic analysis, managing complexity, prioritization, generating solutions, identifying root causes, work subdivision, roadmap generation, and effective communication.

Systematic analysis : Issue trees guide a systematic analysis of the problem. By dissecting the problem into its constituent parts, you can thoroughly examine each aspect and understand its implications.

Managing complexity : Complex problems often involve multiple interrelated factors. Issue trees provide a way to manage this complexity by organizing and visualizing the relationships between different components.

Prioritization : Issue trees help in prioritizing actions. By assessing the importance and impact of each sub-issue, you can determine which aspects of the problem require immediate attention.

Generating solutions : Issue trees facilitate the generation of potential solutions or strategies for each component of the problem. This allows for a more comprehensive approach to problem-solving.

Identifying root causes : Issue trees help in identifying the root causes of a problem. By drilling down through the sub-issues, you can uncover the underlying factors contributing to the main issue.

Work subdivision : Issue trees provide you with a list of smaller, distinct problems or areas to explore. This distinction makes it easy for you to divide up work.

Roadmap generation : Issue trees layout exactly all of the different areas or issues that you need to focus on in order to solve the overall problem. This gives you a clear idea of where to focus your attention and work on.

Effective communication : Issue trees are powerful communication tools. Visualizing the problem in a structured format helps in explaining it to others, including team members, stakeholders, or clients.

How Do I Create An Issue Tree?

Creating an issue tree involves several steps. Here's a step-by-step guide to help you through the process:

Step 1: Define the top-level issue

Start by clearly articulating the main problem or question that you want to address. This will form the root of your issue tree.

Step 2: Identify the branches (sub-issues)

Consider the major sub-issues that contribute to the overall problem. These will become the branches of your issue tree. Brainstorm and list them down.

There are four major ways that you can break down the root problem in an issue tree. You can break down the issue by stakeholder, process, segments, or math.

Stakeholder : Break the problem down by identifying all stakeholders involved. This may include the company, customers, competitors, suppliers, manufacturers, distributors, and retailers. Each stakeholder becomes a branch for the top-level issue.
Process : Break the problem down by identifying all of the different steps in the process. Each step becomes a branch for the top-level issue.
Segment : Break the problem down into smaller segments. This may include breaking down the problem by geography, product, customer segment, market segment, distribution channel, or time horizon. Each segment becomes a branch for the top-level issue.
Math : Break a problem down by quantifying the problem into an equation or formula . Each term in the equation is a branch for the top-level issue.

Step 3: Break down each branch

For each branch, ask yourself if there are further components that contribute to it. If so, break down each branch into more specific components. Continue this process until you've reached a level of detail that allows for meaningful analysis.

Similar to the previous step, you can break down a branch by stakeholder, process, segment, or by math.

Step 4: Review and refine

Take a step back and review your issue tree. Make sure it accurately represents the problem and its components. Look for any missing or redundant branches or sub-issues.

Step 5: Prioritize and evaluate

Consider assigning priorities to different sub-issues or potential solutions. This will help guide your decision-making process.

How Do I Use Issue Trees in Consulting Case Interviews?

Issue trees are used near the beginning of the consulting case interview to break down the business problem into smaller, more manageable components.

After the interviewer provides the case background information, you’ll be expected to quickly summarize the context of the case and verify the case objective. After asking clarifying questions, you’ll ask for a few minutes of silence to create an issue tree.

After you have created an issue tree, here’s how you would use it:

Step 1: Walk your interviewer through the issue tree

Once you’ve created an issue tree, provide a concise summary of how it's structured and how it addresses the problem at hand. Explain the different branches and sub-branches. They may ask a few follow-up questions.

As you are presenting your issue tree, periodically check in with the interviewer to ensure you're on the right track. Your interviewer may provide some input or guidance on improving your issue tree.

Step 2: Identify an area of your issue tree to start investigating

Afterwards, you’ll use the issue tree to help identify a branch to start investigating. There is generally no wrong answer here as long as you have a reason that supports why you want to start with a particular branch.

To determine which branch to start investigating, ask yourself a few questions. What is the most important sub-issue? Consider factors like urgency, impact, or feasibility. What is your best guess for how the business problem can be solved?

Step 3: Gather data and information

Collect relevant facts, data, and information for the sub-issue that you are investigating. This will provide the necessary context and evidence for your analysis.

Step 4 : Record key insights on the issue tree

After diving deeper into each sub-issue or branch on your issue tree, you may find it helpful to write a few bullets on the key takeaways or insights that you’ve gathered through your analysis.

This will help you remember all the work that you have done during the case interview so far. It’ll also help you develop a recommendation at the end of the case interview because you’ll quickly be able to read a summary of all of your analysis.

Step 5: Iterate and adjust as needed

As you work through the problem-solving process, be prepared to adjust and update the issue tree based on new information, insights, or changes in the situation.

Remember, creating an issue tree is not a one-size-fits-all process. It's a dynamic tool that can be adapted to suit the specific needs and complexity of the problem you're addressing.

Step 6: Select the next area of your issue tree to investigate

Once you have finished analyzing a branch or sub-issue on your issue tree and reached a satisfactory insight or conclusion, move onto the next branch or sub-issue.

Again, consider factors like urgency, impact, or feasibility when prioritizing which branch or sub-issue to dive deeper into. Repeat this step until the end of the case interview when you are asked for a final recommendation.

What are Examples of Issue Trees?

Below are five issue tree examples for five common types of business situations and case interviews.

If you want to learn strategies on how to create unique and tailored issue trees for any case interview, check out our comprehensive article on case interview frameworks .

Profitability Issue Tree Example

Profitability cases ask you to identify what is causing a company’s decline in profits and what can be done to address this problem.

A potential issue tree template for this case could explore four major issues:

What is causing the decline in profitability?
Is the decline due to changes among customers?
Is the decline due to changes among competitors?
Is the decline due to market trends?

Market Entry Issue Tree Example

Market entry cases ask you to determine whether a company should enter a new market.

Is the market attractive?
Are competitors strong?
Does the company have the capabilities to enter?
Will the company be profitable from entering the market?

Merger and Acquisition Issue Tree Example

Merger and acquisition cases ask you to determine whether a company or private equity firm should acquire a particular company.

Is the market that the target is in attractive?
Is the acquisition target an attractive company?
Are there any acquisition synergies?
Will the acquisition lead to high returns?

Merger and acquisition issue tree example

New Product Issue Tree Example

New product cases ask you to determine whether a company should launch a new product or service.

Will customers like the product?
Does the company have the capabilities to successfully launch the product?
Will the company be profitable from launching the product?

Pricing Issue Tree Example

Pricing cases ask you to determine how to price a particular product or service.

A potential issue tree template for this case could explore three major issues:

How should we price based on the product cost?
How should we price based on competitors’ products?
How should we price based on customer value?

What are Tips for Making Effective Issue Trees?

Issue trees are powerful tools to solve complex business problems, but they are much less effective if they don’t follow these important tips.

Issue tree tip #1: Be MECE

MECE stands for mutually exclusive and collectively exhaustive. When breaking down the overall problem in your issue tree, the final list of smaller problems needs to be mutually exclusive and collectively exhaustive.

Mutually exclusive means that none of the smaller problems in your issue tree overlap with each other. This ensures that you are working efficiently since there will be no duplicated or repeated work.

For example, let’s say that two of the issues in your issue tree are:

Determine how to increase cups of lemonade sold
Determine how to partner with local organizations to sell lemonade

This is not mutually exclusive because determining how to partner with local organizations would include determining how to increase cups of lemonade sold.

In determining how to increase cups of lemonade sold, you may be duplicating work from determining how to partner with local organizations.

Collectively exhaustive means that the list of smaller problems in your issue tree account for all possible ideas and possibilities. This ensures that your issue tree is not missing any critical areas to explore.

For example, let’s say that you break down the issue of determining how to decrease variable costs into the following issues:

This is not collectively exhaustive because you are missing two key variable costs: sugar and cups. These could be important areas that could increase profitability, which are not captured by your issue tree.

You can read a full explanation of this in our article on the MECE principle .

Issue tree tip #2: Be 80/20

The 80/20 principle states that 80% of the results come from 20% of the effort or time invested.

In other words, it is a much more efficient use of time to spend a day solving 80% of a problem and then moving onto solving the next few problems than to spend five days solving 100% of one problem.

This same principle should be applied to your issue tree. You do not need to solve every single issue that you have identified. Instead, focus on solving the issues that have the greatest impact and require the least amount of work.

Let’s return to our lemonade stand example. If we are focusing on the issue of how to decrease costs, we can consider fixed costs and variable costs.

It may be a better use of time to focus on decreasing variable costs because they are generally easier to lower than fixed costs.

Fixed costs, such as paying for a business permit or purchasing a table and display sign, typically have long purchasing periods, making them more difficult to reduce in the short-term.

Issue tree tip #3: Have three to five branches

Your issue tree needs to be both comprehensive, but also clear and easy to follow. Therefore, your issue tree should have at least three branches to be able to cover enough breadth of the key issue.

Additionally, your issue tree should have no more than five branches. Any more than this will make your issue tree too complicated and difficult to follow. By having more than five branches, you also increase the likelihood that there will be redundancies or overlap among your branches, which is not ideal.

Having three to five branches helps achieve a balance between going deep into specific sub-issues and covering a broad range of aspects. It balances breadth and depth.

Issue tree tip #4: Clearly define the top-level issue

Make sure that you clearly articulate the main problem or question. This sets the foundation for the entire issue tree. If you are addressing the wrong problem or question, your entire issue tree will be useless to you.

Issue tree tip #5: Visualize the issue tree clearly

If you're using a visual representation, make sure it's easy to follow. Use clean lines, appropriate spacing, and clear connections between components.

Keep your issue tree organized and neat. A cluttered or disorganized tree can be confusing and difficult to follow.

Ensure that each branch and sub-issue is labeled clearly and concisely. Use language that is easily understandable to your audience.

Issue tree tip #6: Order your branches logically

Whenever possible, try to organize the branches in your issue tree logically.

For example, if the branches in your issue tree are segmented by time, arrange them as short-term, medium-term, and long-term. This is a logical order that is arranged by length of time.

It does not make sense to order the branches as long-term, short-term, and medium- term. This ordering is confusing and will make the entire issue tree harder to follow.

Issue tree tip #7: Branches should be parallel

The branches on your issue tree should all be on the same logical level.

For example, if you decide to segment the branches on your issue tree by geography, your branches could be: North America, South America, Europe, Asia, Africa, and Australia. This segmentation is logical because each segment is a continent.

It would not make sense to segment the branches on your issue as United States, South America, China, India, Australia, and rest of the world. This segmentation does not follow logical consistency because it mixes continents and countries.

Issue tree tip #8: Practice and get feedback

It takes practice to create comprehensive, clear, and concise issue trees. This is a skill that takes time to develop and refine.

When you initially create your first few issue trees, it may take you a long period of time and you may be missing key sub-issues. However, with enough practice, you’ll be able to create issue trees effortlessly and effectively.

Practice creating issue trees on different problems to improve your skills. Seek feedback from peers or mentors to refine your approach.

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What are the differences between issue trees and hypothesis trees, overview of answers.

Date ascending
Date descending

Issue trees are at the heart of the problem-solving approach. What you refer to in your question are two distict types: diagnostic trees (what you call issue tree --> "problem-based") and solution trees (what you call hypothesis tree --> "solution-based").

Generally speaking, an issue tree is a graphical breakdown of your key question. Trees have four basic rules:

They consistently answer why or how questions (depending on your key question)
They progress from the key question to the analysis as they move to the right
Their branches are mutually exclusive and collectively exhaustive (MECE)
They use an insightful breakdown

Diagnostic trees are for diagnosing your key question

Diagnostic trees help you search for all the possible causes of a problem. They give you the " WHY? ".You list these in logical groups on the first column to the right of your key question, ensuring that your groupings are MECE. As you progress to the right, you drill further down in the details of each grouping.

Solution trees are for actively looking for ways to correct your problem

With solution trees , you look for all the potential solutions to your problem. They give you the " HOW? ". As a general rule, you want to know the WHY before you get to the HOW , so if you don’t know the root cause(s) of your problem, find these first by means of a diagnostic tree.

For broader context, issue trees progress further into details until elements are sufficiently explicit. Then come the hypotheses, analyses and data sources. Once you have tested which solutions are viable, you are ready to select one, for instance by using a decision matrix (impact vs. doability).

Example Problem Tree for a household recycling issue:

Example Solution Tree for a household recycling issue:

Hope this helps!

In practical sense, they are similar.

Imagine building a framework for Market Entry where you look at 4 things as part of a I ssue Tree:

Market Attractiveness
Competitive Landscape

Then you break each one down in more details. You have an issue tree now. You can also state a hypothesis for your interviewer saying: My hypothesis is that our client can enter the Japenese Car Manufacturing market, given that

There is a real Market
We can win in this market
The economics are favourable
And the risks are acceptable

Then you basically test each of these 4 hypotheses by asking the interviewer questions around each bucket ot test it. Does this help? Best, Aws

We might count to the COGS as well factors such as the German Packaging Act (https://www.lizenzero.de/en/blog/german-packaging-act-in-the-uk-obligations-when-shipping-to-germany/) or in general any costs that arise during shipment.

Related Cases

Bain Case Style - Growth offensive at ChemCorp [NEW]

Kearney first round case - top chemicals, related case interview basics article(s).

The Issue Tree Framework can be used to break down the problems of a case to its components and significantly increase your speed during case interviews.

Understanding Evolution

Your one-stop source for information on evolution

The Tree Room

Trees are hypotheses

If you had read about the evolutionary history of whales in the 1970s or 80s, you might have seen a tree that looks something like that shown below left, which implies that whales are closely related to an extinct group of mammals called the mesonychids. Today, we know that the origin of whales is better represented by the tree on the right. Whales and water-loving hippos are closely related! Why the change? Because the discovery of new DNA evidence caused paleontologists to re-evaluate their interpretations of the fossil evidence, leading to a revision of our understanding of the evolutionary relationships in this group.

Furthermore, we know that this branch of the mammal’s evolutionary tree will continue to grow as paleontologists dig further into the fossil record. Most organisms that have ever lived on Earth (including most ancient relatives of whales) have gone extinct. As new fossil organisms are discovered, biologists will have to figure out where on this tree they fit. In fact, we will never know the true evolutionary history of all whale-relatives because of the incompleteness of the fossil record. Many organisms that belong on this tree have gone extinct without leaving a trace in the fossil record. However, it’s important to keep in mind that, although this tree is incomplete and some details may change as we discover new evidence, it is still a good representation of the relationships among these groups of organisms and is based on many different converging lines of evidence.

This example highlights a basic characteristic of evolutionary trees: they are hypotheses that have been tested with evidence. Because they are supported by so many lines of evidence, widely accepted phylogenetic trees are unlikely to have their branches rearranged (though new branches are likely to be added as species are discovered). However, a change in our understanding is always possible. If new evidence is discovered or old evidence is reinterpreted, we must adjust our views of evolutionary relationships to reflect those data. Ignoring evidence would be bad science!

This doesn’t mean that trees change all the time. On the contrary, many evolutionary trees are so well supported (and continue to be supported by newly discovered evidence) that they are very likely to represent the evolutionary relationships among the organisms included accurately. 4 For example, the idea that birds are a twig on the dinosaurs’ branch of the tree of life became widely accepted in the 1980s and 90s based on fossil and anatomical evidence. Subsequent decades have yielded evidence that further supports this hypothesis. Hundreds of feathered dinosaur fossils have been unearthed, and proteins extracted from a Tyrannosaurus rex fossil were found to be remarkably similar to those of a chicken. We’d all better get used to the idea that birds are part of the dinosaur lineage, because all the available evidence suggests that it is true and that the idea is here to stay! New feathered dinosaur species will certainly be discovered and we will need to add them to the tree, but such changes are very unlikely to shake our basic understanding of the close relationship between birds and dinosaurs.

Dinosaur/bird phylogeny and reconstruction of a feathered dino

4 Of course, any phylogenetic tree is likely to be incomplete because of the existence of now-extinct lineages that belong on the tree but that we simply don't know about.

Trees adapted from: Geisler, J.H., and J.M. Theodor. 2009. Hippopotamus and whale phylogeny. Nature 458:E1-E4; Naylor, G.J.P, and D.C. Adams. 2001. Are the fossil data really at odds with the molecular data? Morphological evidence for Cetartiodactyla phylogeny reexamined. Systematic Biology 50:444-453; Zhou, X., S. Xu, Y. Yang, K. Zhou, and G. Yang. 2011. Phylogenomic analyses and improved resolution of Cetartiodactyla . Molecular Phylogenetics and Evolution 61:255-264; Zelenitsky, D.K., F. Therrien, G.M. Erickson, C.L. DeBuhr, Y. Kobayashi, D.A. Eberth, and F. Hadfield. 2012. Feathered non-avian dinosaurs from North America provide insight into wing origins. Science 338:510-514.

Clades within clades

The anatomy of an evolutionary tree

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Who’s Joe?

“A fact is a simple statement that everyone believes. It is innocent, unless found guilty. A hypothesis is a novel suggestion that no one wants to believe. It is guilty until found effective.”

– Edward Teller, Nuclear Physicist

During my first brainstorming meeting on my first project at McKinsey, this very serious partner, who had a PhD in Physics, looked at me and said, “So, Joe, what are your main hypotheses.” I looked back at him, perplexed, and said, “Ummm, my what?” I was used to people simply asking, “what are your best ideas, opinions, thoughts, etc.” Over time, I began to understand the importance of hypotheses and how it plays an important role in McKinsey’s problem solving of separating ideas and opinions from facts.

What is a Hypothesis?

“Hypothesis” is probably one of the top 5 words used by McKinsey consultants. And, being hypothesis-driven was required to have any success at McKinsey. A hypothesis is an idea or theory, often based on limited data, which is typically the beginning of a thread of further investigation to prove, disprove or improve the hypothesis through facts and empirical data.

The first step in being hypothesis-driven is to focus on the highest potential ideas and theories of how to solve a problem or realize an opportunity.

Let’s go over an example of being hypothesis-driven.

Let’s say you own a website, and you brainstorm ten ideas to improve web traffic, but you don’t have the budget to execute all ten ideas. The first step in being hypothesis-driven is to prioritize the ten ideas based on how much impact you hypothesize they will create.

The second step in being hypothesis-driven is to apply the scientific method to your hypotheses by creating the fact base to prove or disprove your hypothesis, which then allows you to turn your hypothesis into fact and knowledge. Running with our example, you could prove or disprove your hypothesis on the ideas you think will drive the most impact by executing:

1. An analysis of previous research and the performance of the different ideas 2. A survey where customers rank order the ideas 3. An actual test of the ten ideas to create a fact base on click-through rates and cost

While there are many other ways to validate the hypothesis on your prioritization , I find most people do not take this critical step in validating a hypothesis. Instead, they apply bad logic to many important decisions . An idea pops into their head, and then somehow it just becomes a fact.

One of my favorite lousy logic moments was a CEO who stated,

“I’ve never heard our customers talk about price, so the price doesn’t matter with our products , and I’ve decided we’re going to raise prices.”

Luckily, his management team was able to do a survey to dig deeper into the hypothesis that customers weren’t price-sensitive. Well, of course, they were and through the survey, they built a fantastic fact base that proved and disproved many other important hypotheses.

Why is being hypothesis-driven so important?

Imagine if medicine never actually used the scientific method. We would probably still be living in a world of lobotomies and bleeding people. Many organizations are still stuck in the dark ages, having built a house of cards on opinions disguised as facts, because they don’t prove or disprove their hypotheses. Decisions made on top of decisions, made on top of opinions, steer organizations clear of reality and the facts necessary to objectively evolve their strategic understanding and knowledge. I’ve seen too many leadership teams led solely by gut and opinion. The problem with intuition and gut is if you don’t ever prove or disprove if your gut is right or wrong, you’re never going to improve your intuition. There is a reason why being hypothesis-driven is the cornerstone of problem solving at McKinsey and every other top strategy consulting firm.

How do you become hypothesis-driven?

Most people are idea-driven, and constantly have hypotheses on how the world works and what they or their organization should do to improve. Though, there is often a fatal flaw in that many people turn their hypotheses into false facts, without actually finding or creating the facts to prove or disprove their hypotheses. These people aren’t hypothesis-driven; they are gut-driven.

The conversation typically goes something like “doing this discount promotion will increase our profits” or “our customers need to have this feature” or “morale is in the toilet because we don’t pay well, so we need to increase pay.” These should all be hypotheses that need the appropriate fact base, but instead, they become false facts, often leading to unintended results and consequences. In each of these cases, to become hypothesis-driven necessitates a different framing.

• Instead of “doing this discount promotion will increase our profits,” a hypothesis-driven approach is to ask “what are the best marketing ideas to increase our profits?” and then conduct a marketing experiment to see which ideas increase profits the most.

• Instead of “our customers need to have this feature,” ask the question, “what features would our customers value most?” And, then conduct a simple survey having customers rank order the features based on value to them.

• Instead of “morale is in the toilet because we don’t pay well, so we need to increase pay,” conduct a survey asking, “what is the level of morale?” what are potential issues affecting morale?” and what are the best ideas to improve morale?”

Beyond, watching out for just following your gut, here are some of the other best practices in being hypothesis-driven:

Listen to Your Intuition

Your mind has taken the collision of your experiences and everything you’ve learned over the years to create your intuition, which are those ideas that pop into your head and those hunches that come from your gut. Your intuition is your wellspring of hypotheses. So listen to your intuition, build hypotheses from it, and then prove or disprove those hypotheses, which will, in turn, improve your intuition. Intuition without feedback will over time typically evolve into poor intuition, which leads to poor judgment, thinking, and decisions.

Constantly Be Curious

I’m always curious about cause and effect. At Sports Authority, I had a hypothesis that customers that received service and assistance as they shopped, were worth more than customers who didn’t receive assistance from an associate. We figured out how to prove or disprove this hypothesis by tying surveys to transactional data of customers, and we found the hypothesis was true, which led us to a broad initiative around improving service. The key is you have to be always curious about what you think does or will drive value, create hypotheses and then prove or disprove those hypotheses.

Validate Hypotheses

You need to validate and prove or disprove hypotheses. Don’t just chalk up an idea as fact. In most cases, you’re going to have to create a fact base utilizing logic, observation, testing (see the section on Experimentation ), surveys, and analysis.

Be a Learning Organization

The foundation of learning organizations is the testing of and learning from hypotheses. I remember my first strategy internship at Mercer Management Consulting when I spent a good part of the summer combing through the results, findings, and insights of thousands of experiments that a banking client had conducted. It was fascinating to see the vastness and depth of their collective knowledge base. And, in today’s world of knowledge portals, it is so easy to disseminate, learn from, and build upon the knowledge created by companies.

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AP®︎/College Biology

Course: ap®︎/college biology > unit 7.

Taxonomy and the tree of life
Discovering the tree of life
Understanding and building phylogenetic trees

Phylogenetic trees

Building a phylogenetic tree

Key points:

A phylogenetic tree is a diagram that represents evolutionary relationships among organisms. Phylogenetic trees are hypotheses, not definitive facts.
The pattern of branching in a phylogenetic tree reflects how species or other groups evolved from a series of common ancestors.
In trees, two species are more related if they have a more recent common ancestor and less related if they have a less recent common ancestor.
Phylogenetic trees can be drawn in various equivalent styles. Rotating a tree about its branch points doesn't change the information it carries.

Introduction

Anatomy of a phylogenetic tree, which species are more related, some tips for reading phylogenetic trees, where do these trees come from, attribution.

" Taxonomy and phylogeny ," by Robert Bear, David Rintoul, Bruce Snyder, Martha Smith-Caldas, Christopher Herren, and Eva Horne, CC BY 4.0 . Download the original article for free at http://cnx.org/contents/[email protected] .
" Organizing life on Earth ," by OpenStax College, Biology, CC BY 4.0 . Download the original article for free at http://cnx.org/contents/[email protected] .

Works cited

John. W. Kimball, "Taxonomy: Classifying Life," Kimball's Biology Pages, last modified December 16, 2013, http://www.biology-pages.info/T/Taxonomy.html .
Jack R. Holt, "Polytomy," Dictionary of Terms,ast revised January 2, 2009, http://comenius.susqu.edu/biol/202/dictionary%20of%20terms/p/polytomy.htm .

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Hypothesis in Machine Learning

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The concept of a hypothesis is fundamental in Machine Learning and data science endeavours. In the realm of machine learning, a hypothesis serves as an initial assumption made by data scientists and ML professionals when attempting to address a problem. Machine learning involves conducting experiments based on past experiences, and these hypotheses are crucial in formulating potential solutions.

It’s important to note that in machine learning discussions, the terms “hypothesis” and “model” are sometimes used interchangeably. However, a hypothesis represents an assumption, while a model is a mathematical representation employed to test that hypothesis. This section on “Hypothesis in Machine Learning” explores key aspects related to hypotheses in machine learning and their significance.

Table of Content

How does a Hypothesis work?

Hypothesis space and representation in machine learning, hypothesis in statistics, faqs on hypothesis in machine learning.

A hypothesis in machine learning is the model’s presumption regarding the connection between the input features and the result. It is an illustration of the mapping function that the algorithm is attempting to discover using the training set. To minimize the discrepancy between the expected and actual outputs, the learning process involves modifying the weights that parameterize the hypothesis. The objective is to optimize the model’s parameters to achieve the best predictive performance on new, unseen data, and a cost function is used to assess the hypothesis’ accuracy.

In most supervised machine learning algorithms, our main goal is to find a possible hypothesis from the hypothesis space that could map out the inputs to the proper outputs. The following figure shows the common method to find out the possible hypothesis from the Hypothesis space:

Hypothesis Space (H)

Hypothesis space is the set of all the possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible (only one) which would best describe the target function or the outputs.

Hypothesis (h)

A hypothesis is a function that best describes the target in supervised machine learning. The hypothesis that an algorithm would come up depends upon the data and also depends upon the restrictions and bias that we have imposed on the data.

The Hypothesis can be calculated as:

[Tex]y = mx + b [/Tex]

m = slope of the lines
b = intercept

To better understand the Hypothesis Space and Hypothesis consider the following coordinate that shows the distribution of some data:

Say suppose we have test data for which we have to determine the outputs or results. The test data is as shown below:

We can predict the outcomes by dividing the coordinate as shown below:

So the test data would yield the following result:

But note here that we could have divided the coordinate plane as:

The way in which the coordinate would be divided depends on the data, algorithm and constraints.

All these legal possible ways in which we can divide the coordinate plane to predict the outcome of the test data composes of the Hypothesis Space.
Each individual possible way is known as the hypothesis.

Hence, in this example the hypothesis space would be like:

The hypothesis space comprises all possible legal hypotheses that a machine learning algorithm can consider. Hypotheses are formulated based on various algorithms and techniques, including linear regression, decision trees, and neural networks. These hypotheses capture the mapping function transforming input data into predictions.

Hypothesis Formulation and Representation in Machine Learning

Hypotheses in machine learning are formulated based on various algorithms and techniques, each with its representation. For example:

Linear Regression : [Tex] h(X) = \theta_0 + \theta_1 X_1 + \theta_2 X_2 + … + \theta_n X_n[/Tex]
Decision Trees : [Tex]h(X) = \text{Tree}(X)[/Tex]
Neural Networks : [Tex]h(X) = \text{NN}(X)[/Tex]

In the case of complex models like neural networks, the hypothesis may involve multiple layers of interconnected nodes, each performing a specific computation.

Hypothesis Evaluation:

The process of machine learning involves not only formulating hypotheses but also evaluating their performance. This evaluation is typically done using a loss function or an evaluation metric that quantifies the disparity between predicted outputs and ground truth labels. Common evaluation metrics include mean squared error (MSE), accuracy, precision, recall, F1-score, and others. By comparing the predictions of the hypothesis with the actual outcomes on a validation or test dataset, one can assess the effectiveness of the model.

Hypothesis Testing and Generalization:

Once a hypothesis is formulated and evaluated, the next step is to test its generalization capabilities. Generalization refers to the ability of a model to make accurate predictions on unseen data. A hypothesis that performs well on the training dataset but fails to generalize to new instances is said to suffer from overfitting. Conversely, a hypothesis that generalizes well to unseen data is deemed robust and reliable.

The process of hypothesis formulation, evaluation, testing, and generalization is often iterative in nature. It involves refining the hypothesis based on insights gained from model performance, feature importance, and domain knowledge. Techniques such as hyperparameter tuning, feature engineering, and model selection play a crucial role in this iterative refinement process.

In statistics , a hypothesis refers to a statement or assumption about a population parameter. It is a proposition or educated guess that helps guide statistical analyses. There are two types of hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1 or Ha).

Null Hypothesis(H 0 ): This hypothesis suggests that there is no significant difference or effect, and any observed results are due to chance. It often represents the status quo or a baseline assumption.
Aternative Hypothesis(H 1 or H a ): This hypothesis contradicts the null hypothesis, proposing that there is a significant difference or effect in the population. It is what researchers aim to support with evidence.

Q. How does the training process use the hypothesis?

The learning algorithm uses the hypothesis as a guide to minimise the discrepancy between expected and actual outputs by adjusting its parameters during training.

Q. How is the hypothesis’s accuracy assessed?

Usually, a cost function that calculates the difference between expected and actual values is used to assess accuracy. Optimising the model to reduce this expense is the aim.

Q. What is Hypothesis testing?

Hypothesis testing is a statistical method for determining whether or not a hypothesis is correct. The hypothesis can be about two variables in a dataset, about an association between two groups, or about a situation.

Q. What distinguishes the null hypothesis from the alternative hypothesis in machine learning experiments?

The null hypothesis (H0) assumes no significant effect, while the alternative hypothesis (H1 or Ha) contradicts H0, suggesting a meaningful impact. Statistical testing is employed to decide between these hypotheses.

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Leaking city pipes appear to be drought-proofing some Austin trees

UT researchers have found trees along Waller Creek are more drought-resistant, likely because of pipes leaking into the system.

Austin loses an estimated 21 gallons of water per person every day, mostly through leaks in the city's water distribution system. Last year that totaled 8.6 billion gallons lost. Those leaks waste more than water; they drain energy and cost money.

But there could be a small silver lining to the big problem of leaks: They appear to be drought-proofing some city trees.

That’s the finding of a team of UT researchers who recently took a look at the rings in trees close to leaky city infrastructure versus trees farther away. The group, headed by Jay Banner, looked specifically at trees along Waller Creek on the UT campus. Banner directs UT’s Environmental Science Institute.

Through isotope testing, Banner determined many local Austin creeks owe much of their flow to water from leaky pipes . He found that 25% to 50% of the water in Waller Creek, which flows through the UT campus and downtown Austin, appears to come from such leaks.

“Because of our initial study, we hypothesized that the trees in the urban watersheds, because of all this leakage, probably wouldn’t be as sensitive to climate changes such as droughts,” Banner told KUT.

The group then took nondamaging core samples of these trees to measure how they had grown through the years by looking at their rings.

“When there’s a drought, the trees don’t grow as fast, so they grow skinnier rings,” Banner said. “So we can use trees as sort of an indicator of past droughts.”

They then compared those samples with core samples from trees in Onion Creek, farther away from the city. What they found appears to confirm their hypothesis.

“The trees in the urban watershed were much less sensitive to drought than the rural watershed trees,” Banner said. “It’s actually a positive consequence of urbanization.”

Does that mean leaks are a good thing?

“Not hardly,” he said.

But he does believe the research could help city planners and water system engineers as they contend with urbanization.

“We know with continued increase in our state’s population there’s going to be more of a demand for water, as well as a demand for places to live,” he said.

Study authors said the research could even give a clearer picture of how leaks form.

“Is this a metric by which we can track degrading urban infrastructure? Its timing? Its extent?” said study co-author Bryan Black, a professor at the University of Arizona’s Laboratory of Tree-Ring Research.

“We start seeing signatures of degradation being recorded in these trees that are time capsules of the environment that they experience,” he said.

The oldest tree in the world: Meet 'Methuselah,' a literal hidden gem.

Trees tell us stories. Scientists count their rings to figure out how old they are and when those years add up, it can be a sign of nature's endurance in the face of change.

Trees that have stood tall through extreme weather, pests, diseases and human interactions, for example, can impart lessons that may help protect future generations of forests. One Florida southern live oak, aptly named “Big Tree,” is over 400 years old . It was struck by lightning at least three times and survived multiple hurricanes and yet remains so healthy that one Michigan non-profit is trying to clone it .

What is the oldest tree?

According to Guinness World Records, the oldest tree species in the world are the bristlecone pines, found in the White Mountains in California. Their scientific name is “Pinus Longaeva."

The world's oldest living tree is "Methuselah." The tree's exact age is unknown, but experts believe it has been alive for close to 5,000 years, the U.S. Forest Service told USA TODAY in an email.

Dendrochronologist Edmund Schulman found and named the tree in 1957 . This bristlecone pine is named after a biblical figure who legend says lived over 900 years. Before that, a geographer in 1964 cut down another ancient tree, dubbed "Prometheus," with permission from the Forest Service . It wasn't until after the tree was cut down that they realized it was an estimated 4,900 years old.

Bristlecone pines are known to scientists as "extremeophiles " because they are slow-growing and can endure harsh environmental conditions, including cold temperatures, high winds, dry soils and short growing seasons, according to the USDA. They also have adapted to use "sectored architecture," which means their roots only feed the part of the tree above them. When one root dies, only that section dies. The rest of the tree keeps growing around the skeletal parts, according to the National Park Service.

Is the oldest tree on earth still alive?

Yes, Methuselah is alive as of May 2024. According to National Geographic, scientists believe this is the planet’s oldest single living thing.

It's in the Inyo National Forest between the Sierra Nevada range in California and the Nevada border. The U.S. Forest Service keeps its exact location under wraps to protect the tree from damage or vandalism, the department confirmed to USA TODAY in an email.

Climate change is threatening the longevity of many of the world's oldest trees, however. Scientists told The Washington Post they fear climate change caused by humans will create dangerous conditions of extreme heat, drought and increased forest fires.

Saving 'Stumpy': Washington residents scramble to save cherry tree

Just Curious for more? We've got you covered.

USA TODAY is exploring the questions you and others ask every day. From "How old is the oldest cat?" to "What is the hardest language to learn?" to "What is the oldest country?" – we're striving to find answers to the most common questions you ask every day. Head to our Just Curious section to see what else we can answer for you.

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May 27, 2024

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Rethinking the sun's cycles: New physical model reinforces planetary hypothesis

by Simon Schmitt, Helmholtz Association of German Research Centres

Researchers at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the University of Latvia have posited the first comprehensive physical explanation for the sun's various activity cycles. It identifies vortex-shaped currents on the sun, known as Rossby waves, as mediators between the tidal influences of Venus, Earth as well as Jupiter and the sun's magnetic activity.

The researchers therefore present a consistent model for solar cycles of different lengths—and another strong argument to support the previously controversial planetary hypothesis. The results have now been published in the journal Solar Physics .

Although the sun, being near to us, is the best researched star, many questions about its physics have not yet been completely answered. These include the rhythmic fluctuations in solar activity . The most famous of these is that, on average, the sun reaches a radiation maximum every eleven years—which experts refer to as the Schwabe cycle.

This cycle of activity occurs because the sun's magnetic field changes during this period and eventually reverses polarity. This, in itself, is not unusual for a star—if it weren't for the fact that the Schwabe cycle is remarkably stable.

The Schwabe cycle is overlaid by other, less obvious fluctuations in activity ranging from a few hundred days to several hundred years, each named after their discoverers. Although there have already been various attempts to explain these cycles and mathematical calculations, there is still no comprehensive physical model.

Planets set the beat

For some years, Dr. Frank Stefani of HZDR's Institute of Fluid Dynamics has been an advocate of the "planetary hypothesis" because it is clear that the planets' gravity exerts a tidal effect on the sun, similar to that of the moon on the Earth. This effect is strongest every 11.07 years: whenever the three planets Venus, Earth and Jupiter are aligned with the sun in a particularly striking line, comparable to a spring tide on Earth when there is a new or full moon. This coincides conspicuously with the Schwabe cycle.

The sun's magnetic field is formed by complex movements of the electrically conducting plasma inside the sun. "You can think of it like a gigantic dynamo. While this solar dynamo generates an approximately 11-year activity cycle in its own right, we think the planets' influence then intervenes in the workings of this dynamo, repeatedly giving it a little push and thus forcing the unusually stable 11.07-year rhythm on the sun," Stefani explains.

Several years ago, he and his colleagues discovered strong evidence of a clocked process of this kind in the available observation data. They were also able to correlate various solar cycles with the movement of the planets just using mathematical methods. At first, however, the correlation could not be sufficiently explained physically.

Rossby waves on the sun act as intermediaries

"We have now found the underlying physical mechanism. We know how much energy is required to synchronize the dynamo, and we know that this energy can be transferred to the sun by so-called Rossby waves. The great thing is that we can now not only explain the Schwabe cycle and longer solar cycles but also the shorter Rieger cycles that we hadn't even considered previously," says Stefani.

Rossby waves are vortex-shaped currents on the sun similar to the large-scale wave movements in the Earth's atmosphere that control high- and low-pressure systems.

The researchers calculated that the tidal forces during the spring tides of two of each of the three planets Venus, Earth and Jupiter had exactly the right properties to activate Rossby waves—an insight with many consequences.

First of all, these Rossby waves then achieve sufficiently high speeds to give the solar dynamo the necessary impetus. Second, this occurs every 118, 193 and 299 days in accordance with the Rieger cycles that have been observed on the sun. And thirdly, all additional solar cycles can be calculated on this basis.

All cycles explained by a single model

This is where mathematics comes in: The superimposition of the three short Rieger cycles automatically produces the prominent 11.07-year Schwabe cycle. And the model even predicts long-term fluctuations of the sun because the movement of the sun around the solar system's center of gravity causes a so-called beat period of 193 years on the basis of the Schwabe cycle.

This corresponds to the order of magnitude of another cycle that has been observed, the Suess-de Vries cycle.

In this context, the researchers discovered an impressive correlation between the 193-year period that had been calculated and periodic fluctuations in climate data. This is another robust argument for the planetary hypothesis because "the sharp Suess-de Vries peak at 193 years can hardly be explained without phase stability in the Schwabe cycle, which is only present in a clocked process," Stefani estimates.

Does this mean the question as to whether the sun follows the planets' beat has finally been answered? Stefani says, "We'll probably only be 100% certain when we have more data. But the arguments in favor of a process clocked by the planets are now very strong."

Journal information: Solar Physics

Provided by Helmholtz Association of German Research Centres

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Hypotheses trees
A hypothesis tree is a powerful tool for top-down problem-solving in business. It's used to help teams target a problem and break it down into sub-hypotheses to make it easier to solve. Think of hypothesis trees as a shortcut—teams create a theory about what's causing the problem based on their experience and use it to form a hypothesis on ...
How to Develop Your Hypothesis Tree
A hypothesis tree takes a problem statement and comprehensively disaggregates potential solutions. Any time you are trying to understand a problem and opportunities better, a hypothesis tree is a great tool. Most projects at strategy consulting firms start with the team spending a few hours brainstorming and aligning on the hypothesis tree for the defined problem statement.
The Definitive Guide to Issue Trees
Issue Trees (also known as "Logic Trees" and "Hypothesis Trees") are THE most fundamental tool to structure and solve problems in a systematic way. Mastering them is a requirement if you want to get a job in a top consulting firm, such as McKinsey, Bain and BCG.
Issue Trees
The issue tree, a species of logic tree in which each branch of the tree is an issue or question, bridges the gap between structure and hypothesis. Every issue generated by a framework will likely be reducible to sub issues, and these in turn may break down further. An issue tree is simply the laying out of issues and subissues into a MECE ...
Consulting Hypothesis Tree: Everything You Need to Know
A hypothesis tree is a powerful problem-solving framework used by consultants. It takes your hypothesis, your best guess at the solution to your client's problem, and breaks it down into smaller parts to prove or disprove. With a hypothesis tree, you can focus on what's important without getting bogged down in details.
Hypothesis Trees Explained: Your Key to Strategic Problem-Solving
Introduction to "Hypothesis Trees": Get up to speed on the fundamentals of "hypothesis trees" and how they can revolutionize your approach to "problem-solving." Step-by-Step Guide: Follow a detailed, step-by-step process for constructing "hypothesis trees" tailored to your specific needs.
MECE Framework McKinsey
Hypothesis tree. Another method to structure a problem is to develop a hypothesis tree, which is the graphical representation of all MECE hypothesis that elucidates the problem. It is, in a way, similar to an issue tree, where a problem is broken down into its components, which makes identifying and solving it easier.
Issue Tree in Consulting: A Complete Guide (With Examples)
It can be presented vertically (top-to-bottom), or horizontally (left-to-right). An issue tree systematically isolates the root causes and ensures impactful solutions to the given problem. The issue tree is most well-known in management consulting, where consultants use it within the "hypothesis-driven problem-solving approach" - repeatedly ...
How to Write a Strong Hypothesis
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
Issue tree
An issue tree, also called logic tree, is a graphical breakdown of a question that dissects it into its different components vertically and that progresses into details as it reads to the right.: 47 Issue trees are useful in problem solving to identify the root causes of a problem as well as to identify its potential solutions. They also provide a reference point to see how each piece fits ...
Issue Trees: Step-By-Step Guide with Examples (2024)
Each segment becomes a branch for the top-level issue. Math: Break a problem down by quantifying the problem into an equation or formula. Each term in the equation is a branch for the top-level issue. Step 3: Break down each branch. For each branch, ask yourself if there are further components that contribute to it.
Trees as hypotheses
Trees as hypotheses. At the most basic level, phylogenetic trees represent hypotheses about evolutionary history. For example, the tree at left below represents the hypothesis that chimpanzees and bonobos are very closely related to one another and that, of the groups shown, humans are their closest living relatives.
What I learned at McKinsey: How to be hypothesis-driven
There is a repeating cycle of forming and testing hypotheses. McKinsey consultants follow three steps in this cycle: Form a hypothesis about the problem and determine the data needed to test the ...
Issue Tree Explained: The Ultimate Guide Including Examples [2023
Issue tree s, sometimes referred to as "issue maps", are a logical structure and powerful tool that help you to identify the different elements of a problem in order to help solve it. They are commonly used in hypothesis based problem solving and are actually considered a type of hypothesis tree. Issue trees start by defining what the problem ...
What are the differences between issue trees and hypothesis trees
Issue trees are at the heart of the problem-solving approach. What you refer to in your question are two distict types: diagnostic trees (what you call issue tree --> "problem-based") and solution trees (what you call hypothesis tree --> "solution-based"). Generally speaking, an issue tree is a graphical breakdown of your key question.
Trees are hypotheses
Trees are hypotheses. If you had read about the evolutionary history of whales in the 1970s or 80s, you might have seen a tree that looks something like that shown below left, which implies that whales are closely related to an extinct group of mammals called the mesonychids. Today, we know that the origin of whales is better represented by the ...
Hypothesis Testing
Table of contents. Step 1: State your null and alternate hypothesis. Step 2: Collect data. Step 3: Perform a statistical test. Step 4: Decide whether to reject or fail to reject your null hypothesis. Step 5: Present your findings. Other interesting articles. Frequently asked questions about hypothesis testing.
How McKinsey uses Hypotheses in Business & Strategy by McKinsey Alum
And, being hypothesis-driven was required to have any success at McKinsey. A hypothesis is an idea or theory, often based on limited data, which is typically the beginning of a thread of further investigation to prove, disprove or improve the hypothesis through facts and empirical data. The first step in being hypothesis-driven is to focus on ...
Phylogenetic trees
A phylogenetic tree is a diagram that represents evolutionary relationships among organisms. Phylogenetic trees are hypotheses, not definitive facts. The pattern of branching in a phylogenetic tree reflects how species or other groups evolved from a series of common ancestors. In trees, two species are more related if they have a more recent ...
Problem solving around hypothesis tree || Yash Chaudhary || ZORBA
A hypothesis tree is the set of all MECE hypotheses that can explain a particular problem. Instead of organizing your analysis around issues or areas such as...
PPTX Hypothesis Trees
Hypothesis Trees can help us make sense of what is going on within a family and provides us with a more structured approach to our evidence gathering, analysis and completion of assessments. A Hypothesis Tree takes a problem, identifies the possible causes of the problem and helps to work out what action is needed to rule the hypothesis in or ...
Hypothesis in Machine Learning
A hypothesis is a function that best describes the target in supervised machine learning. The hypothesis that an algorithm would come up depends upon the data and also depends upon the restrictions and bias that we have imposed on the data. The Hypothesis can be calculated as: y = mx + b y =mx+b. Where, y = range. m = slope of the lines.
Decision Trees in Machine Learning: Two Types (+ Examples)
Shaped by a combination of roots, trunks, branches, and leaves, trees often symbolise growth. In machine learning, a decision tree is an algorithm that can create classification and regression models. The decision tree is so named because it starts at the root, like an upside-down tree, and branches off to demonstrate various outcomes.
Leaking city pipes appear to be drought-proofing some Austin trees
"So we can use trees as sort of an indicator of past droughts." They then compared those samples with core samples from trees in Onion Creek, farther away from the city. What they found appears to confirm their hypothesis. "The trees in the urban watershed were much less sensitive to drought than the rural watershed trees," Banner said.
Dark forest hypothesis
Dark forest hypothesis. The dark forest hypothesis is the conjecture that many alien civilizations exist throughout the universe, but they are both silent and hostile, maintaining their undetectability for fear of being destroyed by another hostile and undetected civilization. [1] It is one of many possible explanations of the Fermi paradox ...
What is the oldest tree in the world? Meet Methuselah
What is the oldest tree? According to Guinness World Records, the oldest tree species in the world are the bristlecone pines, found in the White Mountains in California. Their scientific name is ...
Rethinking the sun's cycles: New physical model reinforces planetary
The researchers therefore present a consistent model for solar cycles of different lengths—and another strong argument to support the previously controversial planetary hypothesis.