Tag Archives: Fact-Based Thinking

Awareness of Awareness Statistics

I often talk about how we can become better consumers of information. One subtle way that information is often presented in a misleading manner is through what I will call “awareness statistics.” These statistics inform you about the number of people who “know of someone” who knows someone.

You hear these awareness statistics all the time. You hear that x of every y people know someone who has suffered from cancer, or abuse, or gun violence, or sexism, or ageism, or police brutality, or has been unfairly profiled, or has been burgled, or who uses personal pronouns.

While all of these issues I cited as examples are real and are important, drawing conclusions – both qualitative and quantitative – from these kind of awareness statistics can be very misleading. Worse, these kind of statistics are often intended to mislead, to exaggerate, and to induce a heightened reaction.

In very rare situations, awareness statistics can be legitimate. They can tell us how deeply a particular narrative has seeped into a population. It can tell us how many people are aware of a particular issue.

But that is not generally, or even often, the point of these statistics. Typically the point of citing such statistics is to serve as a surrogate for direct measurement. Rather than directly reporting the number of people who have been injured in motorcycle accidents, we report how many people know of someone who has been injured in a motorcycle accident. The intent is not to measure mere awareness, but to convey an impression of actual accident frequency.

The underlying problem is that awareness relationships in a population are extremely complex, highly uneven, and skewed. Some few people have many more relationships than others. We simply cannot correlate “awareness” with actual frequency in any straight-forward manner. If Britney Spears tweets about her bad hair day, millions of people know of someone who had a bad hair day. If Nicki Minaj tweets about her friend’s testicular reaction to the Covid vaccine, tens of millions of people “know someone” who had a terrible reaction to the vaccine.

Consider the example of sexual behavior. Experts strongly suspect that a relatively few men have relationships with a much larger number of women. No one knows the exact numbers, but let’s just make up some to illustrate. Let’s say that 1 guy has affairs with 10 women during a period of time. Each woman tends to share this information with 5 close friends. Now, when surveyed, 50 women report that they “know of someone” who has had an affair. It sounds like lots of guys are having affairs, but it’s really just that one really horny studmuffin. Most women are led to believe that lots of guys are having affairs and most of the guys are wondering why they are such losers at love.

So how should we assimilate such awareness statistics?

First, You should be skeptical whenever you hear awareness statistics. Actively skeptical. It is not enough that you merely be aware of their limitations, because they can still be successful in creating a lasting misleading impression despite your academic skepticism. You must not only be aware of their limitations, but actively suspicious of them.

You should always ask whether awareness statistics are being presented simply because we cannot measure the actual number directly. If that is the case, you should consider this to be no more than a very unreliable indicator.

But if awareness statistics are being presented despite the fact that the actual number can be directly measured, then you should assume that the intent is to manipulate your reaction. If advocates report that 2.5 million people know someone who knows someone who has been murdered, that sounds far more alarming then saying there were 1000 murders committed. It is their intent to alarm you when the raw numbers are insufficiently alarming.

Finally, resist the urge to accept statistical exaggerations when you support the cause and even when you think people need to be more alarmed. The problem is that the other side can play the same game. Anything you can exaggerate with awareness statistics, they can exaggerate just as easily. Sixty-five million people know someone who has been a victimized by cancel culture and 27 million people know of someone who was saved by a hero with a handgun.

Stay true to real facts. Don’t be swayed by manipulative statistics – especially when you believe in your heart that some exaggeration is warranted. After all, over 45 million people know of someone who knows someone who has been a victim of awareness statistics.

Better yet, just don’t use them at all unless you are a sophisticated demographer.

Humans are Inexplicable

brainWhether it be in science or business or politics or popular culture, we expend an inordinate amount of time and effort trying to figure out why people do whatever people are doing. We seem to have more analysts than actors, all desperately trying to explain what motivates people, either by asking them directly or by making inferences about them. For the most part, this is not merely a colossal waste of time and effort and money in itself, but it stimulates even greater wastes of time and effort and money chasing wildly incomplete or erroneous conclusions about why we do what we do.

Asking people why they did what they did, or why they are doing what they are doing, or why they are going to do what they are going to do, generally yields useless and misleading information. It is not clear that people actually have distinct reasons they can recognize let alone articulate. It is quite likely in fact that most of the decisions we make are made unconsciously based upon a myriad of complex neural network associations. These associations need not be rational. These connections don’t need to be internally consistent to each other or related to the actual outcome in any way. But in our post-rationalizations and post-analyses we impose some logic to our decisions to make them feel sensible. Therefore, the reasons we come up with are almost completely made-up at every level to sound rational or at least sane to ourselves and to those we are communicating to.

The truth is, we can’t usually hope to understand our own incredibly complex neural networks, let alone the neural networks of others. Yes, sometimes we can identify a strong neural network association driving a behavior, but most determinative associations are far too diffuse across a huge number of seemingly unrelated associations.

The situation gets infinitely worse when we are trying to analyze and explain group behaviors. Most of our shared group behaviors emerge from the weak-interactions between all of our individual neural networks. The complexity of these interactions is virtually unfathomable. The challenge of understanding why a group does what it does collectively, let alone figuring out how to influence their behavior, is fantastic.

If you ask a bird why it is flying in a complex swirling pattern along with a million other birds, it will probably give you some reason, like “we are looking for food,” but in fact it is probably largely unaware that it is even flying in any particular pattern at all.

So why point all this out? Do we give up? Does this imply that a rational civilization is impossible, that all introspection or external analysis is folly?

Quite the contrary, we must continue to struggle to understand ourselves and truly appreciating our complexity is part of that effort. To do so we must abandon the constraints of logic that we impose upon our individual and group rationalizations and appreciate that we are driven by neural networks that are susceptible to all manner of illogical programming. We must take any self-reporting with the same skepticism we would to the statement “I am perfectly sane.” We should be careful of imposing our own flawed rationality upon the flawed rationality of others. Analysts should not assume undue rationality in explaining behaviors. And finally, we must appreciate that group behaviors can have little or no apparent relationship to any of the wants, needs, or expressed opinions of those individuals within that group.

In advanced AI neural networks, we humans cannot hope to understand why the computer has made a decision. Its decision is based upon far too many subtle factors for humans to recognize or articulate. But if all of the facts programmed in to the computer are accurate, we can probably trust the judgement of the computer.

Similarly with humans, it may be that our naive approach of asking or inferring reasons for feelings and behaviors and then trying to respond to each of those rationales is incredibly ineffective. It may be that the only thing that would truly improve individual and thus emergent thinking are more sanely programmed neural networks, ones that are not fundamentally flawed so as to comfortably rationalize religious and other specious thinking at the most basic levelĀ (see here). We must focus on basic fact-based thinking in our educational system and in our culture on the assumption that more logically and factually-trained human neural networks will yield more rational and effective individual and emergent behaviors.