Storytell In Reverse
Second, we learn to tell stories in reverse using the fishbone diagram analysis. This is a powerful tool to deal with the problem of cause and effect and illuminate our thinking to the possibilities of alternative perspectives.
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For those of us who are more artistically inclined, this is your moment to shine.lving. Fleshing out a list of all the possible causes of a problem simultaneously provides you with a blueprint of the specific factors you need to focus on to ultimately find viable solutions.
The fishbone diagram is so structured that those causes are placed in categories, so you get a more orderly perspective of the entire situation. It’s a more organized way of working in reverse from effect to cause and is a frequently used tool for structuring brainstorming sessions. The end product is a visual display of all the factors—both from a micro and a macro perspective—that play a role in the effect or the problem.
To make a fishbone diagram, first write a problem statement or effect somewhere in the middle right portion of a whiteboard or any writing surface you’ve chosen. Draw a box around it, then a horizontal line across the page that ends in that problem box. That box will serve as the “head” of the fishbone.
rized under. You can write the same cause under multiple categories, if applicable. Then, for each noted cause, continue asking what might’ve caused it and write it down as a connection to that cause—and so on until you can no longer think of a more primary cause. This will allow you to exercise your deductive reasoning skills until you arrive at the most fundamental root causes of the problem.
When you’re done with the diagram, scrutinize the causes you’ve listed and consider the evidence. How much does the identified cause really contribute to creating the effect? Is its link with the problem well-established and significant enough to consider seriously? Get into the habit of thinking, “What would make this cause a true and significant factor in the problem at hand?”
For example, say you’re a hotel manager trying to understand the causes of low customer satisfaction ratings for your hotel service. Write the problem in a box as the fishbone “head” and the categories of possible causes (in this case, the four Ps of service industries) as the main “bones.” Doing this, the initial stages of your fishbone diagram would look like so:
Then start filling in each category with possible causes. For example, you’ve identified that possible causes for the problem are (1) the slow resolution of customer complaints and (2) the hotel staff’s inability to be sensitive to the customers’ needs, thus leading the customer to become dissatisfied with the service.
Asking yourself why your staff may lack sensitivity to customer needs, you may consider that they work such long hours that they are reduced to providing the bare minimum of service; they no longer have enough energy to pay more attention to customers’ specific needs. Given that, your fishbone diagram would now indicate the following:
e, at its bones. you to concretely trace how the problem is linked back to specific causative factors.
Try watching a scene, a person, or any other thing and observing ten details about it. Then, for each of those specifics, write down five possible causes that may have led that particular detail to be the way it is. Try to vary the potential causes you list, ranging from the plainly realistic to the downright bizarre. This will train your ability to create a story around every detail and consider what preceded it, thus exercising your skills in reverse storytelling.
Separate Correlation from Causationvent we’re looking at. This is what we should spend our time trying to fix, but it turns out that we might be spending all of our energy on the wrong issue. We’re fooled into confusing corr
elation for causation. One of this mental model’s shining examples follows.
Say you’re looking at a graph that depicts two data comparisons—one axis shows the total number of sunglasses sold over a period of time, and the other shows the total sales of ice cream. During the summer months, you note that sales of both items increase and that they tend to go down after summer is over.
Looking at this graph, you might come to the conclusion that sales of ice cream directly impact sales of sunglasses. People are buying more sunglasses because they’re buying more ice cream—or the other way around. No matter the direction, it appears that one is causing the other.
Why might this be the case? Is it because there are stores that sell both ice cream and sunglasses? Is there something about buying a sundae or root beer float that triggers one to grab a pair of Ray-Bans immediately after? Do sunglasses press on a facial nerve that triggers sugar cravings?
These theories sound ridiculous, don’t they? That’s because they are.
When you first read the example, you probably figured out that sales of ice cream and sunglasses increased due to the arrival of summer. Since there are more hot and sunny days in summer, people are more inclined to buy cold treats like ice cream and protective eyewear like sunglasses. People don’t buy sunglasses as a direct result of ice cream purchases—they buy both when the summer heat hits them. Just because two things occur simultaneously doesn’t mean there is a causal relationship between them.
Even though that’s a pretty broad example, it reflects a logical error that lots of people make—sometimes about matters even more elementary and basic than ice cream and sunglasses. That error is believing that since two events have similar patterns or related behaviors, one must be causing the other to happen. This is the mistake of believing that correlation implies causation. In fact, they are entirely separate concepts.
Correlation is a statistical term. It shows that two individual elements or variables share similar traits or trends—“ice cream and sunglasses sales both increased.” That’s all there is to correlation: two things behave similarly in this way or that way. Correlation does not describe why or how the relationship between two items is the way it is; it doesn’t give a reason. It just says, “These two things are generally doing the same thing at the same time.”
Causation, on the other hand, is an effort to establish the reason things happen—also referred to as “cause and effect.” The message of causation is: “This thing changed, which in turn caused this other thing to change.” In our super-basic example, the thing that actually caused the increase in sunglasses revenue was the arrival of summer, which was also responsible for the boost in ice cream sales. There was a causal relationship between summer and sunglasses and summer and ice cream, but there’s only a correlative relationship between sunglasses and ice cream.
To believe that the increase in ice cream sales caused the rise in sunglasses sales is a logical mistake. This is countered by the phrase correlation doesn’t imply causation—just because two events are similar doesn’t mean one is causing the other one to happen. There may be another underlying factor that’s causing both things to happen.
This error in thinking usually happens when there’s a lack of information at our disposal—or, perhaps more frequently, when we don’t take the time to observe all the information we should. Jumping to conclusions is always a temptation when we feel under pressure to come up with a definitive answer. In order to avoid that fallacy, one should identify as many potential factors as one can: research, study trends, gather more data, and make reasonable, unhurried judgments.
In a lot of cases, correlations are nothing more than flukes or chance, yet we rapidly jump to causal conclusions. When evaluating cause and effect, the default mental model should always be to separate correlation from causation and not assume a causal relationship unless you can definitively say so.
There’s one more wrinkle when it comes to discussing cause and effect. It’s a bit more complex than we’re led to believe as children, when we’re taught that pushing on a toy truck will make it move.
As we gain more life experience, causal factors become a little more complicated. There are more conditions, underlying motives, and elements that affect events. Sometimes it’s hard to point to a singular cause, because it’s hard to say that element acted alone or wasn’t the product of multiple mini-causes.
This process involves looking past the immediate reason things happen (the proximate cause) and searching for a greater, more fundamental basis that makes things happen (the root cause). The proximate cause is to the root cause as correlation is to plain causation. Solving for the former (proximate cause; correlation) won’t rid you of your troubles.