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We have been talking about the SPIRAL model since May, and this is the last installment on that. I have to thank you all for the emails on this topic. There is a lot to it, and you have clearly been paying attention judging from your excellent and insightful questions. I urge you to ask them IN THE BLOG, which will allow me to answer them there so that all can benefit. I will always answer emails, though and they are the best way to get an answer on a sensitive subject.
This time, we will address the two last types of inferences that we will cover: Analogy and Narrative. We have been covering the inferences from strongest (most desirable) to weakest, and so far have discussed inference from Example, from Cause, and from Correlation (also called Inference from Sign).
Analogies are a kind of comparison. We use them to compare a complex or unfamiliar concept that we want the listener to understand to a concept with which they are already familiar. The hope is that the listener will come to understand the unfamiliar concept by accepting that it is comparable to the familiar one. That acceptance of comparability is how we connect the evidence to the original claim, which is the purpose of an inference. Here is a quick example:
Our claim is – Nuclear reactors are very safe.
Our evidence is – the number of nuclear accidents since the beginning of the nuclear age compared with other kinds of accidents (like household).
Our Inference – A FAR larger number of accidents happen in American homes EACH MINUTE than the number of nuclear accidents that have happened since the beginning of the nuclear age.
Here we have used an analogy without actually stating something like “We can measure safety for nuclear reactors just as we measure household safety – by the number of accidents.”
Clearly this is a bad comparison – and a bad analogy. The potential impact of a SINGLE nuclear accident is many times higher than household accidents that occur in an entire year. One kind of “accident” does not compare with another kind of “accident”, even though they are both called “accidents”.
The test for a good analogy is simple “Do essential similarities outweigh essential differences?” Are there essential differences in the things being compared (in this case, household accidents and nuclear accidents)? When you read through the example, you may have “felt” there was something wrong with the argument but couldn’t put your finger on it. It is the essential difference between the two kinds of accidents that probably made you feel that. If you DIDN’T think anything was wrong with the argument, then you can see how you can buy into a bad argument by an analogy.
Analogies are weak because, at best, they are just comparisons of resemblances between things. You cannot “see” an analogy like you can see an example. You cannot test an analogy, like you can test a causal inference. You cannot measure an analogy, like you can measure a correlation. Still they are stronger than our last inference – Narratives.
A narrative is a story. It is designed to draw the listener in and give support to a claim by offering the listener with the opportunity to accept the story as adequate support for my claim if, and only if, the story is plausible.
Let’s say your claim is that “Hard work benefits those who engage in it” – and you offered as inference a narrative. We have all heard the fable of the Ant and the Grasshopper (if you haven’t, go here). It ends with the idea that “Just as the ant that works hard and doesn’t stop to play like the foolish grasshopper will be carried through lean times on the results of his hard work, you too will benefit by not resting while there is still work to be done.”
The story seems plausible – someone that works would likely be better off in hard times than someone that wouldn’t. The story is coherent. That is, all the pieces of it make sense with each other – nothing is inherently contradictory or counter-intuitive. The characterizations are consistent – the grasshopper doesn’t do anything strangely out of character, nor does the ant. And finally, the story resonates with us. It appeals to my sense of fairness that a hard-working person (or insect) should be rewarded for their effort.
And, these are the tests of a good narrative:
• Is the narrative plausible?
• Is the narrative coherent?
• Are characterizations consistent?
• Does the narrative have resonance?
You will find it very hard to convince someone of anything that is very important if all you have to offer is story. How about if I offered testimonials from a few hardworking people (or foolish grasshopper types) as examples? Or if I could demonstrate how hard work “causes” success? Or if I could show statistics indicating how hard workers do better in hard times? Or if I could compare the results of hard work and the results of leisure and draw an analogy with a relationship that you are very familiar with so that you could clearly understand it? Those inferences (again, from strongest to weakest, Example, Cause, Correlation, and Analogy) are all stronger.
If you are doing the talking, pick the strongest one you can. If you are doing the listening, test them as I have indicated. If you are refuting, use the strongest inference that you can AND ask why the other side hasn’t used stronger inferences to support THEIR side. Listeners will presume it is because there isn't any strong reason to support the other side.
Congratulations! This newsletter will be the last one on the SPIRAL model of critical discussions for a while. The topic for the NEXT series will be Persuasion. We will start on the fundamentals of persuasion next time!
Insist on great business results! Go to Pathfinder Communication
Monday, October 25, 2010
Sunday, October 10, 2010
Inferences of Cause and Correlation
Last time we spoke about the strongest kind of inference; one of example. The reason it is potentially the strongest is because by definition, an example exists. Sometimes we ask people to use reason alone to create an inference (that is, to connect evidence to a claim). Given the choice, evidence is more compelling if it can be viewed as something that exists to directly support the claim instead of something that can be reasoned to support the claim.
This time, we’ll talk about two more inferences; inference of Cause and inference of Correlation. Causal inference describes the instance when one thing causes another. That is one thing (like a rainstorm) causes another (like a flood) to happen. Note that many things can cause a flood, one of them being a rainstorm. Many, many times things that CAN cause others to happen are NOT the cause in a given instance. Let’s say that your sales manager says that “sales are terrible right now because of the economy.” You can look through your company information and see sales are indeed down, and you can look in the paper and see that the economy is in trouble. Both those things are happening.
However, is there any proof (valid causal inference) that the economic problems CAUSED the low sales? In our example, not yet.
In order to show that the economy is responsible for our sales dip, we would have to ask:
-Are there other things that could cause low sales?
-Have any of them happened?
-If so, how do we know it is not one of those?
If the only thing that can cause low sales is the economy AND we now that the economy is floundering, then we can accept the inference because there is nothing else that can cause the phenomenon of low sales. Intuitively, though, we know there are more things, for instance:
-Product quality issues
-Competition and Disruptive technologies
-Poor customer service
-Poor sales staff performance
Knowing that there are alternative causes doesn’t tell us that the economy isn’t the cause, but it does tell us that it COULD be something else. One would need to look at the alternative causes and analyze their contribution (if any) to the issue of low sales before one could really accept the causal inference of poor economy to low sales.
For this reason, cause is difficult to analyze thoroughly in a fast paced business environment and thorough analysis is sometimes not done in favor of dealing with the suspected cause. This is sometimes referred to as “jumping to solutions” and often can worsen the symptoms.
In our case, let’s say that the economy did affect our customers, but also a competitor had released a competing product with a few features that the customers liked and we were having a customer service problem as well. Reacting as if the problem was purely one of economics (and therefore would go away when the economy picks up) could have very serious consequences to our business. This is the fallacy of false cause.
In testing casual inferences, it is critical to determine that one thing indeed influences another, and isn’t merely being attributed. For instance, the “economy vs. sales levels” question that I raised above is too complex to be so simply explained away. Sales level rise and fall based on many things, and there most certainly are many factors that can cause price fluctuations. In fact, a strong argument can be made that sales levels frequently drop after a period when there has been too much speculation and the market needs to “normalize” in order to get healthy. So if you plan on attributing lowered sales levels to a single cause, then you need to be able to at least give some reasoning as to why the drop shouldn’t be attributed to any other reason and bring lots of evidence.
One must evaluate whether there are multiple causes and not just one. For instance, while it is true that the economy can cause sales levels to drop, so can poor customer support. If customer service was unsatisfactory to the point that customers started looking for other suppliers, it may not be the economy that caused your sales levels to go.
One must also remember that one cause can have multiple effects, for instance turning off the water main in order to stop a pipe from leaking and spilling water on the floor. It will work because there is certainly a causal relationship between the presence of water in the leaky pipe and water on the floor, but there are other effects that shutting the water main have that are perhaps unwanted.
One must evaluate whether there are common causes underlying the supposed cause and effect relationship. For example, I recently read that American babies with low birth weight tend not to grow up to go to college. Reading this, I immediately thought that a common cause underlying both conditions (low birth weight and failure to go to college) is poverty. In checking, I found that the author had misinterpreted some statistics and “bent them” to try to show why it is important for mother’s to try to achieve higher birth weights!
One must evaluate whether one is confusing temporality with causality. In other words, just because something happens BEFORE something else, doesn’t mean it is the CAUSE of it. Just because I correct someone’s work and later I start having trouble with them on the job doesn’t mean that the two events are causally
related. It could be that, once I correct them, my attitude changes about them
and they pick up my attitude. Or that the reason I am correcting them is that they are overwhelmed by the work and they would rather work somewhere else and eventually this starts to manifest as “trouble” to me.
There are many fallacies associated with causal inference. Here are two:
- The Post Hoc fallacy – (if something occurs BEFORE an event, then it means it CAUSED the event.) This fallacious thinking is used all the time. When you hear
it, question it. Just because the shack burned down after Johnny was seen near
it doesn’t mean that Johnny is guilty of burning it down. Nor does it mean he is innocent.
- Appeal from Ignorance – (Well, if the economy DIDN’T cause sales levels to drop, then what did?) This appeal is meant to put the listener in a position of either offering an alternate explanation or accepting the one offered. Remember there is a third choice – that something else caused the event and you don’t know what it is. You needn’t ever buckle to the appeal from ignorance.
Correlation differs from cause in that does NOT try to explain a relationship between two things like cause does (one thing CAUSES another). It merely
describes that two things tend to occur with each other. Correlation is called “Inference of Sign” for this reason – it says that one thing is a SIGN of another, without explaining the relationship.
My favorite example of this is the relationship of college degrees to competence. If a person has a college degree in a given subject, we accept it as a sign that they have expertise in the subject. Is it certain that they do? No. Are there other things that can account for expertise? Yes? However, when all things are considered, a college degree is a SIGN of expertise in that subject. We aren’t trying to explain the relationship. That is trying to say that college degrees CAUSE people to be competent. We are just expressing that one thing (competence) tends to accompany another (a degree) and that we can count on that relationship holding within certain limits.
How do we test correlative inference? The classic tests for Inference from Sign are:
- Does the sign usually appear with the thing signified (ex: college degree and expertise)?
- Does the sign frequently appear without the thing signified (ex: college degree and incompetence)?
- Are there countersigns (absence of college degree and expertise)?
- Could the correlation be a coincidence?
- Is it really a causal relationship?
Insist on great business results! Go to Pathfinder Communication
This time, we’ll talk about two more inferences; inference of Cause and inference of Correlation. Causal inference describes the instance when one thing causes another. That is one thing (like a rainstorm) causes another (like a flood) to happen. Note that many things can cause a flood, one of them being a rainstorm. Many, many times things that CAN cause others to happen are NOT the cause in a given instance. Let’s say that your sales manager says that “sales are terrible right now because of the economy.” You can look through your company information and see sales are indeed down, and you can look in the paper and see that the economy is in trouble. Both those things are happening.
However, is there any proof (valid causal inference) that the economic problems CAUSED the low sales? In our example, not yet.
In order to show that the economy is responsible for our sales dip, we would have to ask:
-Are there other things that could cause low sales?
-Have any of them happened?
-If so, how do we know it is not one of those?
If the only thing that can cause low sales is the economy AND we now that the economy is floundering, then we can accept the inference because there is nothing else that can cause the phenomenon of low sales. Intuitively, though, we know there are more things, for instance:
-Product quality issues
-Competition and Disruptive technologies
-Poor customer service
-Poor sales staff performance
Knowing that there are alternative causes doesn’t tell us that the economy isn’t the cause, but it does tell us that it COULD be something else. One would need to look at the alternative causes and analyze their contribution (if any) to the issue of low sales before one could really accept the causal inference of poor economy to low sales.
For this reason, cause is difficult to analyze thoroughly in a fast paced business environment and thorough analysis is sometimes not done in favor of dealing with the suspected cause. This is sometimes referred to as “jumping to solutions” and often can worsen the symptoms.
In our case, let’s say that the economy did affect our customers, but also a competitor had released a competing product with a few features that the customers liked and we were having a customer service problem as well. Reacting as if the problem was purely one of economics (and therefore would go away when the economy picks up) could have very serious consequences to our business. This is the fallacy of false cause.
In testing casual inferences, it is critical to determine that one thing indeed influences another, and isn’t merely being attributed. For instance, the “economy vs. sales levels” question that I raised above is too complex to be so simply explained away. Sales level rise and fall based on many things, and there most certainly are many factors that can cause price fluctuations. In fact, a strong argument can be made that sales levels frequently drop after a period when there has been too much speculation and the market needs to “normalize” in order to get healthy. So if you plan on attributing lowered sales levels to a single cause, then you need to be able to at least give some reasoning as to why the drop shouldn’t be attributed to any other reason and bring lots of evidence.
One must evaluate whether there are multiple causes and not just one. For instance, while it is true that the economy can cause sales levels to drop, so can poor customer support. If customer service was unsatisfactory to the point that customers started looking for other suppliers, it may not be the economy that caused your sales levels to go.
One must also remember that one cause can have multiple effects, for instance turning off the water main in order to stop a pipe from leaking and spilling water on the floor. It will work because there is certainly a causal relationship between the presence of water in the leaky pipe and water on the floor, but there are other effects that shutting the water main have that are perhaps unwanted.
One must evaluate whether there are common causes underlying the supposed cause and effect relationship. For example, I recently read that American babies with low birth weight tend not to grow up to go to college. Reading this, I immediately thought that a common cause underlying both conditions (low birth weight and failure to go to college) is poverty. In checking, I found that the author had misinterpreted some statistics and “bent them” to try to show why it is important for mother’s to try to achieve higher birth weights!
One must evaluate whether one is confusing temporality with causality. In other words, just because something happens BEFORE something else, doesn’t mean it is the CAUSE of it. Just because I correct someone’s work and later I start having trouble with them on the job doesn’t mean that the two events are causally
related. It could be that, once I correct them, my attitude changes about them
and they pick up my attitude. Or that the reason I am correcting them is that they are overwhelmed by the work and they would rather work somewhere else and eventually this starts to manifest as “trouble” to me.
There are many fallacies associated with causal inference. Here are two:
- The Post Hoc fallacy – (if something occurs BEFORE an event, then it means it CAUSED the event.) This fallacious thinking is used all the time. When you hear
it, question it. Just because the shack burned down after Johnny was seen near
it doesn’t mean that Johnny is guilty of burning it down. Nor does it mean he is innocent.
- Appeal from Ignorance – (Well, if the economy DIDN’T cause sales levels to drop, then what did?) This appeal is meant to put the listener in a position of either offering an alternate explanation or accepting the one offered. Remember there is a third choice – that something else caused the event and you don’t know what it is. You needn’t ever buckle to the appeal from ignorance.
Correlation differs from cause in that does NOT try to explain a relationship between two things like cause does (one thing CAUSES another). It merely
describes that two things tend to occur with each other. Correlation is called “Inference of Sign” for this reason – it says that one thing is a SIGN of another, without explaining the relationship.
My favorite example of this is the relationship of college degrees to competence. If a person has a college degree in a given subject, we accept it as a sign that they have expertise in the subject. Is it certain that they do? No. Are there other things that can account for expertise? Yes? However, when all things are considered, a college degree is a SIGN of expertise in that subject. We aren’t trying to explain the relationship. That is trying to say that college degrees CAUSE people to be competent. We are just expressing that one thing (competence) tends to accompany another (a degree) and that we can count on that relationship holding within certain limits.
How do we test correlative inference? The classic tests for Inference from Sign are:
- Does the sign usually appear with the thing signified (ex: college degree and expertise)?
- Does the sign frequently appear without the thing signified (ex: college degree and incompetence)?
- Are there countersigns (absence of college degree and expertise)?
- Could the correlation be a coincidence?
- Is it really a causal relationship?
Insist on great business results! Go to Pathfinder Communication
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