Thursday, October 15, 2015

Generalizations: The Good, the Bad, the Ugly, and the 1950s

Greetings!

I interrupt my 31 Days of Halloween to bring you a discussion topic that I have seen circulating quite a lot as of recent. In fact, this also goes hand in hand with my post on lie spotting, which I would suggest reading first before continuing with this one. Yes, there is a method to my madness here. ;)

Lie Spotting or "Yes, You've Been Lied to...and You Probably Liked It

And as an added bonus, here is another video with Pamela Meyer as she explains how to spot a liar:




So in continuing on, I will continue this series in which I discuss how things like lie spotting and generalizations are very much intertwined with one another and how they have much to do with what I discuss in this blog and even a little in some of my own fiction writing. And as you probably guessed in the title, I will also be using the 1950s as an example later on. Why? Because as a writer and actress, I frequently study human behavior, especially in recent years. And one thing that both fascinates and perplexes me is this love/hate attitude that our modern society seems to have toward that particular era or decade. But more on that later (I'm even listening a Buddy Holly, Little Richard and Everly Brothers mix as I write this).

Now as a review, in my blogpost on lie spotting, I discuss Pamela Meyer's TED Talk on the subject. In the speech, she discusses how humans are at their most vulnerable and tend to let their guard down the most when he or she hearing what he or she wants to hear about him or herself. Whatever that may be. Why? Because we crave validation in one area or another of our lives. We like to feel as though we are worth something and sometimes looking at our own accomplishments on our own is sometimes not enough. We need to actually hear about how awesome we are. We need to have someone recognize that we did something worthy and that we did a good job doing it. Truly, whether we wish to admit to it or not, we thrive on the opinions of others, particularly if those opinions are praise. In order for a lie to work, there has to be two willing and active participants. The one doing the lying and the one willing to be lied to.
And Meyer discusses that while you can't eradicate all the liars of the world, one way to arm yourself against them is by coming to recognize where you are the most vulnerable. What do you want to hear the most about yourself? What do you want to hear so much that it would literally make you putty in the hands of a possible con artist? Coming to recognize this may actually be difficult and even painful. But in the end, it will allow you to not only turn on your bullshit barometer, but also strengthen it. Which in turn will allow you to grow and be a much stronger individual.

Now what does all that have to do with the subjects of generalizations and the 1950s? I will be getting to that at the end of this post, but for now, let's get into generalizations and how they can both help and harm society.
Throughout this piece, I will be referencing an article from the website, Fallacy Detective. The article is written by Hans Blueborn, and I think he does a really good job with breaking down generalizations, how they can be both good and bad, and how what is called "hasty generalizations" can be downright harmful and fallible.

In the article, Blueborn starts out by stating how in our everyday lives, we tend to generalize things. In fact, most decisions that we make is based on a generalization. And Blueborn uses salesmen (or if you really want to be politically correct, then sales"person" it is...so I don't want to hear it from the PC police) as an example and how people tend to generalize them in a category of individuals that don't give a lick about the people they sell to, all they want is your money.
And how do they reach that conclusion? Well, maybe they had such an experience with a few. Add to that, maybe their friends and family also had similar experiences. Therefore, we feel validated in such a generalization.
And generalizations don't always have to be bad or wrong. In fact, sometimes they can be useful. For example, here is an example where Blueborn discusses this (and think of the concept of lie spotting while you read this passage from his article):

We make predictions on what somebody is going to do before they do it, and sometimes we are right. For instance: you are playing a game of Clue with Jenny and Bert. Bert, after one of his turns, lays down his cards with a crooked smile and begins to study the ceiling unconcernedly. 

You have seen that look before. That means he has the solution and is about to win the game.
Next turn, he wins. You knew he would because you generalized. You noticed that he did those things in past games when he won, and you generalized that he would do the same this time.

See? Generalizations don't always have to be wrong. In fact, they can really be of a benefit a person if applied correctly and with care. But when can they do more harm than good? And what exactly is a "hasty generalization?"

Well, Blueborn goes on to say the following:

When you examine examples of people or things in a class, you are taking a "sample" from which you make a generalization about the things in the class. You say: "All salesmen (the class here is "salesmen") are money grubbers" or, "92.93% of salesmen are money grubbers" – based on your generalization.
Unless you know what you are doing, taking samples and making generalizations can be a risky business. When our samples and generalizations are not conducted properly, they are called a "hasty generalization." 

He then goes on to explain a few facts on understanding generalizations and how you can possibly make good ones. The first point is that generalizations must rely on samples which are taken of a class and not necessarily studying every individual or every thing in a class. For example, if someone surveyed a hundred construction workers on their education level and all of those particular construction workers replied that they had little to no education beyond high school, the person conducting the survey wouldn't be off base by saying that construction workers have a limited education. He or she would simply be stating what he or she knew to be true based on his or her own personal experience.

The second point Blueborn makes is that generalizations can be strong or weak and that they are useful because you don't HAVE to study every single thing in a class before making a conclusion. Getting back to the construction worker example, the surveyor in question did not have to examine the education history of every last construction worker before reaching the conclusion that construction workers have a limited education. In fact, attempting to examine the educational backgrounds of every construction worker out there would be tedious to say the least. So he or she takes what he or she knows and generalizes.
But here's the catch:  one can never REALLY know whether a generalization is completely correct until they actually do examine every subject in a class. In fact, if the person surveying the education level of construction workers found even just one that had a Doctorate in Astrophysics or any other kind of college degree, that alone would be enough to weaken the original conclusion and generalization. And if the surveys continued, he or she may find even more construction workers with education well beyond high school.
So does that make the generalization automatically wrong? No. But it does weaken it.
As Blueborn states, a generalization can't really be true of false. Only strong or weak.

With that in mind, we move onto point number three, which states that any generalization must be overthrown - or at least adjusted - by a single contrary case.
In this third point, Blueborn explains the following:

If I studied 3,000,000 politicians all over the world and I found that they were all corrupt, I might generalize that all politicians are corrupt. But, if I found a single politician who wasn't corrupt (maybe he was living off in the jungle somewhere and didn't show up for the census), I would have to throw out my generalization – or at least modify it: "All politicians – except one I know of who lives in the jungle – are corrupt." Or, "All the politicians whom I have studied are corrupt." Or, better yet, "Most politicians are corrupt."

Finally, he reaches the fourth and final point that a generalization can then become stronger by finding a larger sample and finding more representative samples:

A good generalization is one which examines a large sample that is spread out over all corners of the class being studied.


So in knowing this, let's take a look at hasty generalizations, what exactly they are, and how they can be harmful:

The most common logical fallacy is the hasty generalization...A hasty generalization is one in which someone generalizes about a class or group – say, "all Italians" – based on a small and poor sample; perhaps just the Italians that live next door.

We also generalize brands:



Examples of this can be found everywhere (a generalization) – especially when we buy things. We tend to make generalizations about brands.
Out here in the wilds of Illinois, people tend to generalize a lot about brands. All farmers own at least one pickup truck – a major pickup to drive – and several minor "backup" pickups which serve time as lawn ornaments.
Every farmer will bear allegiance to one brand of pickup over another. Certain farmers are Ford farmers and others are Chevy farmers. (A growing minority are Dodge farmers.) Each thinks his brand is the best.
And the reason for their allegiance? "Well," Farmer McDonald will say, "I once owned a Ford, and it was junk. Now I only drive Chevies."
So, all of Farmer McDonald's experience with Ford trucks came from this single sample. Is that a good basis to judge all Ford trucks? When pressed further, Farmer McDonald will confess: his unfortunate Ford really was junk, 15-year-old junk, bought used, and all Farmer McDonald's subsequent Chevies have been bought new and sold early.
So his sample of Ford trucks may not be representative.
Farmer Brown, down the road, has a similar story: "I only buy Ford trucks. I once owned a Chevy and it was junk." Of course, farmer Brown's dilapidated Chevy had also seen better days before he nursed it home for the first time.
Maybe Ford trucks are junk. Maybe Chevy trucks are junk. But while Farmer McDonald and Farmer Brown may be right to call their own trucks "junk," they need to see many more trucks before they can accurately say one brand is better than the other. Their generalization is hasty.



Now that we hopefully have a better understanding of generalizations, let us move on to what a hasty generalization is.
One way that people make hasty generalizations is by jumping to a conclusion with too small of a sample. If the sample of the class we are studying is not a large one, there is much risk in then finding a sample that is not representative to those in the class that is being looked at.

Blueborn puts it brilliantly and I think better than I could (I will link to the entire article at the end of this post):

We all know that tossing a coin will result in it landing half the time heads, and half the time tails. However, this does not mean that if we toss it four times we will see heads twice and tails twice. Even if we toss it a dozen times, we might not see an equal number of heads and tails. In order to actually see the heads and tails even out, we need to toss it many, many more times – say, a hundred or a thousand or more – and even then we may be off a few. 

Obviously, Farmer McDonald and Farmer Brown did not have enough samples for their generalizations. The trucks they bought simply may have been the duds which come out of all factories, or worn-out second-hand vehicles.


Now stemming from point number two of a hasty generalization is that the samples is not often - or sometimes even ever - a good representation of the class as a whole.
Blueborn concludes his article with the following:

In a generalization, sometimes the sample is large enough, but it isn't representative of the entire class. When making generalizations, people very often will study only samples near at hand, or easy to get to. Oftentimes, this will not give a good picture of the entire class being studied, which will make the resulting generalization lopsided.

For example: If I wanted to know the eating habits of Italians, it would be very easy for me to study the Italians who live in my town. However, there are many Italians in the world: those who live in America and have or have not adhered to their regional fare; those who still live in Italy; those who are or aren't in the Mafia; those who are on a diet....
The Italians in my town may not eat the same things as those who live somewhere else. My study could conclude that most Italians eat spaghetti, when in reality, just those in my town do. And maybe not even all of them. Or maybe everywhere else they eat pizza and ravioli. Maybe I only studied Italians when they ate supper and found out that they ate spaghetti then – but the rest of the time they ate pop tarts.

Farmer McDonald and Farmer Brown's trucks also weren't representative of the "average" truck of their kind. There are many kinds of trucks in various states of decay. Their trucks were old and broken down. They would need to see a few examples which were not.


So now that we have a better understanding of generalizations, why we make them, how they can or cannot hurt, and how they may or may not be valid, here is where my fascination with our society's love/hate attitude toward the 1950s come in, and this seems to be the case with the 50s more than any other era. It also seems to be the time that suffers from the most generalizations, whether those generalizations are positive or negative.
I will get more into this in the next post when I will be getting into looking at and explaining the moral nature of our society. But for now, I will leave you with the following scenario to think about until then:

Two individuals, now in their seventies, are discussing growing up in the 1950s to their millennial generation grandchildren.
One of these individuals says the following (and keep in mind, what these grandparents say will very likely shape how the grandchild views the era in question):

"When I was growing up, people were hard-working and did what needed to be done to support the family, whether it was outside or inside the home. Children were able to run outside and play in the streets until it was dark outside without really having to worry about things like drive-by shootings. There was more of a sense of family and community then there is today and back then, marriage was forever, people read more instead of being glued to the television for hours at a time, and kids actually did their chores and had respect for their elders. People were also accepting and kind to one another. Not rude like you see today."

Now the other individual says the following:

"In the 50s, it was not uncommon for a man to beat his wife and children nearly to death. Women were prisoners in their homes and were also often raped and sexually abused by their husbands, with no consequence for the husband. Victim blaming was everywhere and women were also denied education. It was also considered respectable to look down on someone what wasn't white. I'm so glad those days are over."

Now with the info in this blogpost and the concept of lie spotting in mind, really give this some thought. Which hypothetical grandparent is right and which is wrong? Or is either right or wrong?

Stay tuned for when I bring the concept of morality into the picture. But for now, I will leave it here.

Til next time.

Referenced Article:
Generalizations by Hans Blueborn, Fallacy Detective
http://www.fallacydetective.com/news/read/generalizations 


Pamela Meyer's Website:
http://liespotting.com/








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