A scientist uses an experiment to test a hypothesis; a consultant uses an issue tree. An issue tree lays out a set of logical conditions that, if proven correct, prove the hypothesis correct. The term “issue tree” comes from the way such a logical structure looks when diagramed on paper – like a tree on its side. Another way to look at an issue tree is as a logical argument that can be tested with data.

The logic of an issue tree and a hypothesis is analogous to the “if/then” statement common in arguments and proofs:

Issue tree branches = if

Hypothesis = then If these three factors (Branch 1, Branch 2, Branch 3) are true, then the hypothesis is true. The if/then construction provides a clear, logical structure that can be proven or disproven with data. This is the essence of problem structuring using issue trees – you make a logical argument based on the hypothesis that can be easily validated with concrete data.

Your Hypothesis

More important than memorizing the main case frameworks is the ability to take your hypothesis and build an issue tree (or customize a standard framework) that will test your hypothesis in this specific case.

The MECE Test

MECE is a principle used by management consulting firms to describe a way of organizing information. The MECE principle suggests that to understand and fix any large problem, you need to understand your options by sorting them into categories that are:

Mutually Exclusive – Items can only fit into one category at a time

And

Collectively Exhaustive – All items can fit into one of the categories

MECE is a systematic framework that helps solve complex problems. By creating an issue tree where each branch represents an exclusive group of factors, you will be able to narrow it down to the single factor that is causing the problem. If there is an overlap in categories, you won’t be able to specifically point out which area the problem is arising from.

To put it simply, a MECE set is one that has no overlaps and no gaps.

The Victor Cheng “Conclusiveness” Test

When looking at your issue tree, say the following statement to yourself: “If all the branches of the issue tree turn out to be true, I can’t imagine a scenario in which the opposite of my hypothesis would be true.” If that statement is valid, then you have a good issue tree that will produce relatively conclusive results.

Let’s take a look at two examples. One example passes the conclusiveness test, and the other does not.

The only factor that changed between these two examples was the competition. In the first example, we can definitively say that, if all of those factors are true, there’s no way that the hypothesis is false.

However, in the second example, even if all of these factors are true, it’s possible that Apple should not enter the tablet market. Maybe Amazon.com’s tablet is going to be priced higher, but it may have features for which people are willing to pay extra. If that’s the case, the competition would have an advantage that could be a real threat to Apple’s success. Ultimately, this test will help you discern:

• Whether you’ve missed any important factors that should be included in your issue tree;
• Whether your issue tree definitely proves your hypothesis;
• Whether you need to remove anything from your issue tree that does not help prove or disprove the hypothesis.

If your issue tree does not fulfill these three requirements, then its validity decreases substantially.