It is something, likely, as old as stoves. Most folks quickly learn not to touch hot stoves.
Actually, it is not so important today as it was a couple of centuries ago when ole Ben first started building stoves. Generally speaking, when someone would touch a hot stove, they were not apt to repeat it.
Actually, I suspect it went back even farther than that. Before there were stoves, there were fireplaces. Before fireplaces campfires, or their equivalent.
I even heard a tale of one of the big wigs at Levi learning not to kneel next to campfires…first time. It was then that they decided to remove one or two of the rivets from the area just below the fly of their famous canvas trousers.
The one thing brought away from the first experience was the probability of pain, sometimes a little embarrassment too. However, here’s the news. Not all stoves are hot. Not all rivets are hot. It just is that once exposed to these experiences we mostly come away thinking they are, or at least can be. It is referred to as inductive reasoning. Because the first stove we touch is hot, we assume all stoves are hot.
What if the reverse is true. What if the first stove you touch is ambient temperature? Do we then assume that all stoves are cool to the touch. If we do this, we expose ourselves to many painful experiences. This is called inductive reasoning.
While it is useful, it can easily lead to errors. For instance, if we see a brown Labrador retriever, it would be wrong to assume that all dogs are brown and weigh eighty pounds. Indeed, it would be wrong to assume that all Labs are brown. Oddly there are some that are black.
On the other hand, suppose we touch a hundred cool stoves. Can we then assume are stoves are cool? If we see a hundred brown Labs, are we to assume that all labs are brown.
You see, even though we see a large number of examples, we cannot truly assume anything.
Until we see a large enough number of examples, we cannot positively say that we know all labs are brown and that all stoves are cool. Even when working with large numbers, inductive reasoning can lead us astray.
I wish that kids in the eighth grade were required to spend a few hours learning about inductive and deductive reasoning. I am convinced the concept is extremely important in so many parts of life.
Let’s take for instance, the woman that is robbed by an African American. Is it right for her to be afraid of all African Americans? Of course, not. Yet, it may take her years to get over the experience. Our fears are not always founded on good logic. Indeed, her fear might keep her from many good friendships.
The somewhat opposite of inductive reasoning is deductive reasoning. In deductive reasoning, we draw conclusions from many, perhaps exhaustive numbers of examples. It is best that these examples are at random. It is the way that medical research is done. I suppose we can say that statistics and deductive reasoning are interrelated. The more the examples and the more random, the more accurate will be the stats deductive reasoning that depends on the stats.
If we have a random selection of a million dogs, it is likely that only a few will be Labs and we will likely see a few black dogs, white dogs and even a few multi-color dogs. Therefore, we can have a more accurate idea of the coloring of dogs. If we take a random measurement of a million stoves, we might actually find that only 30% are hot enough to cause pain, or even discomfort. (only a wild guess, not am actual statistic)
I’m not going to try to create an equivalent example with the thievery. It’s far too complex and there are too many ways it can go wrong with my imaginary statistics. Moreover, I am not going to suggest that a woman should get robbed a million times. Two or three maybe, but no more. Still, the principles remain firm. With a larger number of examples, we would be able to draw more accurate deductions.
However, we need to be careful about drawing snap conclusions. When we go from the millions of examples and try to derive a single situation from millions of examples, we can still be wrong. For instance, if I may. It would not indicate that a thief is of any ethnicity, and it would be wrong to make any such suggestion.
Yet, every day, I see some people blame Black men because of individual as well as vast statistical data. Those methods just don’t work. And, by the way, the methods don’t work on Caucasian policemen, again, regardless of past inductive or deductive reasoning. You cannot convict a policeman based on past experience just as the woman cannot convict based on past thieves.
Perhaps the most horrible example of inductive reasoning is when the person says, “Single parent families are just as good as two-parent families.” Then they go about calling out two, three or four examples of good kids brough up by single parents. That logic has two holes. First, it is based on a very small count of examples. Second, there is the probability that, if there is a second parent, the child would likely have turned out better. The statistics back it up. We are talking millions of examples not just two or three.
On the other side of the coin, I see people say that a particular person turned out good or bad because of his parent(s). The stats prove that some good kids come from bad or broken homes and bad kids come from homes with good parents.
In this case, the inductive logic gets us nowhere and the deductive logic only shows trends. The trend shows overwhelmingly that two parent homes are better. But logic tells us that it is only true if they are good parents. Abusive and or alcoholic parents rarely qualify as good parents. Yet, again, some good kids come from homes with abusive parents. Sorry. I have no explanation for that. I’m not sure there is one.
For those who are not truly familiar with the terms inductive and deductive reasoning, may I suggest you take an hour or two and look into it on the net. Most will find it far more complex than most of us realize. For instance, one thing that must accurately be determined in inductive reasoning is an accurate correlation. For instance, that dance by that Voo-do doctor likely has nothing to do with that solar eclipse. On the other hand, all that rain I dumped on my lawn the other day likely had nothing to do with the thunderstorm we got the next day, though it did seem a little coincidental. If we collected enough data, it is likely to be proved that the one thing had nothing to do with the other.