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Category Archives: Single-Event Fallacy

Vaccinations, Autism, Psychics, and Silver Bullets

To solve complex problems, engineers try to identify all of the important variables that might be in play. Working from this list, each variable is analyzed and tested to determine its effect, if any, on the problem, until the root cause of the problem is identified. The root cause is often traced to a dominant variable, a Big Gorilla. Sometimes, however, it’s not possible to clearly identify a root cause, either because an important variable was not on the list, or because the cause is a combination of variables, and that combination was not considered.

Because we are prone to thinking there is always a single silver-bullet solution to every problem (see “The Single-Event Fallacy (Am I Psychic?)” in this post: “I’m Right! (Or Am I?)“), the possibility of a problem being caused by a combination of significant variables is often disregarded, making problem-solving efforts ineffective and even misleading. For example, a scientific study of thimerosal (50% mercury) in vaccinations may conclude that there is no correlation to autism, and the media will then shout, “Vaccinations don’t cause autism!” But this conclusion is not scientifically justified. For example, is there something else in vaccinations — either by itself or in combination with other factors, including thimerosal — that is linked to autism?

Therefore, in addition to all of the standard skeptical questions one should ask about any study (e.g., was there a control group? were statistics used properly? was the study funded by an organization that has a stake in the results?), it’s always good to be cautious about accepting overly broad conclusions from a study where only a single variable was considered.

Regarding autism and vaccinations, researchers have recently identified a correlation between the Haemophilus influenzae type b (Hib) vaccine and autism (“Hypothesis: Conjugate vaccines may predispose children to autism spectrum disorders,” discussed here). Hib can now be added to the list of possible significant variables (see “Off Topic: The Autism Epidemic) that may play a significant role in the perplexing and devastating puzzle of autism.

-Ed Walker

 

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Cause or Coincidence?

Humans appear to be wired, for survival reasons, to jump to conclusions based on personal experience, particularly regarding potential threats. Eons ago, Harry eat red berries and Harry fall down, have convulsions, and die. Conclusion: We no eat red berries. Can’t argue with that. Although other varieties of red berries may provide wonderful nourishment, why take a chance?

Equating consumption of red berries with dying is an example of a correlation; an observed apparent coupling of one thing to another. As statisticians like to say, however, correlation is not proof of causation. The classic example of this is the observation that the rooster crows at dawn, i.e. there is a correlation between the rooster crowing and the sun peeping over the horizon. A primitive person might observe this correlation and conclude that the rooster’s crowing causes the sun to come up. Actually, as we know, the reverse is true; i.e. the rooster’s crowing is a result, not a cause. Therefore, to learn the correct lessons from observing correlations, it’s important to discern cause from effect.

In other instances, a correlation is simply a coincidence; i.e. no true correlation exists. For example, someone dreams that a loved one will be in an accident, and subsequently they are. Although the dream is observed to correlate with the subsequent unfortunate event, in truth this was just a coincidence (see “The Single Event Fallacy” in “I’m Right! (Or Am I?)”). But the person who had such a dream will (unless they employ Engineering Thinking) understandably be very likely to conclude that they’ve experienced a profound psychic event.

If an engineering design exhibits problems, engineers are very careful to thoroughly examine the situation. They can’t afford to confuse cause and effect, or to assume that correlations exist when they may not. Instead, they study and test until they have a reasonable certainty that they truly understand the root cause of the problem. This understanding allows engineers to devise effective solutions.

A misguided attempt to fix a problem, without understanding the true root cause, can make things worse instead of better. An even more unfortunate result is when a fix superficially appears to work, wherein in reality it introduces hidden defects. Later in time — after the supposed fixers have taken their bows and are long gone from the scene — the hidden defects erupt, wreaking havoc. Because of the time delay, the defects may not be perceived as having originated with the earlier faulty fix. This is indeed a tragic outcome: a supposed solution is perceived as being successful, when in reality it made things worse.

An Action’s Success Should Not Be Judged
On Whether Or Not It Appears To Improve Things In The Short Run,
Instead It Should Be Judged On Whether Or Not
(A) The Improvement Is Maintained Over Time, And
(B) The Improvement Is Superior To Alternative Actions (Including Doing Nothing)

For example, the conventional wisdom is that during the Great Depression president Franklin D. Roosevelt helped guide the economy to recovery by vigorously inserting the federal government into economic affairs. FDR initiated a myriad of intrusions, such as “work relief” programs; jobs that were funded by the government. Eventually — many years later, during World War 2 — the economy finally did indeed improve. Some observers thought, wow, FDR didn’t spend enough federal money, because it wasn’t until the world war started and federal spending went up even more, that we finally got ourselves out of the depression. In other words, there was a perceived correlation between massive federal spending and the end of the depression. But was this correlation properly interpreted? Was federal spending the root cause of the recovery?

In our next post we’ll take a look at how an engineering team might address that question.

Next Post:

A Brief Engineering Review of Economic Meltdowns

-Ed Walker

 

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I’m Right! (Or Am I?)

If you have strong opinions and want to test them scientifically, drill down to their roots and check them against the following list of unscientific justifications:

Unscientific Justifications

  1. That’s the way I was raised
  2. I work in a union and all my friends feel the same way
  3. I work in a corporate office and all my friends feel the same way
  4. Oprah feels the same way
  5. I read it in a best-selling book
  6. I saw a “based on a true story” movie
  7. Because that’s the way things should be

If your opinions are based on unscientific justifications, that doesn’t mean you are wrong. It does mean that you will probably have a very hard time defending your opinions when discussing them with others. And if those others also use fallacy-laden arguments, everyone will experience a lot of sound and fury, signifying nothing (my apologies to William Shakespeare).

“But Ed,” you protest, “I don’t use unscientific justifications; I’m on guard against those. I base my opinions on facts, not emotion.”

Sounds good, but do you, really? Take a look at the following list of possibly unscientific justifications:

Possibly Unscientific Justifications

  1. It happened to me once
  2. I read it in a magazine
  3. I learned it in a seminar
  4. I read it in a textbook
  5. I watched a documentary
  6. It was reported in the newspaper
  7. My doctor told me
  8. A scientist said so on TV
  9. I read it on a “fact check” Internet site
  10. Some reviews on an Internet site said it was great

Because the principle of objectivity is so vital in the decision-making process, we’ll review the above list in more detail. Let’s start with “It happened to me once.”

Learning from An Experience

A personal experience can be a wonderful teacher. However, as real and powerful as an experience is, it is a sample of one. It’s only one experiment, if you will.

“What are you taking about, Ed?” you exclaim loudly. “If it happened to me — if I experienced it myself, saw it with my own eyes — then it’s got to be true!”

Not necessarily. Human perceptions are imperfect, and unless we’re careful we tend to jam our experiences into preconceived boxes that fit our expectations. Our memories are likewise imperfect, and tend to adapt to what we want to remember, rather than retain the reality of what really happened.

Nonetheless, for simple phenomena, one experiment is sometimes sufficient to reach an important conclusion. If you put your hand on a hot stove and get burned, a valid conclusion is — don’t do that again!

The Single-Event Fallacy (Am I Psychic?)

For complex phenomena, however, deriving a firm conclusion from a sole personal experience is an example of the single-event fallacy. For example, what if you dreamed that you were going to have a fight with your spouse in the morning, and sure enough, you did. Can you conclude that you’re psychic? Not really, because in this instance there are many variables involved. Dreams, for example, often mirror common events such as arguments with spouses, and there are billions of us dreaming such dreams every night. According to the laws of probability it’s quite likely that some folks will, on rare occasion, have a dream that coincidentally matches upcoming reality.

True psychic ability would be indicated by predictive dreams or visions that cannot be explained by coincidence; e.g. dreams of improbable events with very specific details (“On Tuesday I dreamed that on Thursday afternoon I would be in an auto accident involving a red sedan driven by a stocky man wearing a gray turtleneck sweater, and it happened!”) If you have such dreams, the next step would be to demonstrate your psychic predictive power to an objective independent observer; i.e. be tested. If you pass you will not only make history, you will make a lot of money — a one-million dollar prize is available to anyone who can prove, under scientific conditions, that they are psychic, or have any other paranormal ability (see JREF). (This prize has been offered for many, many years and there have been no successful applicants.)

Conclusion: When multiple variables are involved, engineering thinking requires the use of numerous samples (experiences) to fashion a reasonable hypothesis for the cause of an event.

Single Experiences Of Complex Events

Do Not Lead To Reliable Conclusions

We’ll continue our review of possibly unscientific justifications in the following posts.

Next Post:

Books, Magazines, and Seminars: Who Do You Trust?

p.s. If you are curious to learn more about the engineering mind, please check out the DACI Newsletter; you may find the Sightings and News Bullet sections interesting.

-Ed Walker

 

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