 I want to talk a little bit about probabilities. When people talk about probabilities, they tend to associate them with things that are obviously random, right? So flipping a coin, rolling a dice, whether your particular stock is going to go up or down, and we think in terms of probabilities where those things are concerned. But where other things in life are concerned, actually I think where most things in life are concerned, we tend not to think in terms of probabilities, but absolutes, right? So you say things like, it's going to rain today. How do you know it's going to rain today? Well, because they said in the news it's going to rain today. Really, it's a probabilistic situation. There's a probability that will rain, there's a probability that won't, right? Now we understand that if it's a 50-50. But if you get up in the morning and it's completely overcast and dark and scary skies and all of this, and the guy in the news says it's going to rain today, and I ask you is it going to rain today, your response is likely to be yes. I say, are you sure? And you say, yes, I'm sure it's going to rain today. Look at it. And I say, is it absolutely going to rain today? And you say, absolutely it's going to rain today. And that's where we go off the rails. Our tendency is when probabilities get particularly high or particularly low, we immediately go to thinking in terms of absolutes. And the fact is there are no absolutes anywhere. You tell your friend, I'll meet you downstairs at noon for lunch. Are you going to meet your friend downstairs at noon for lunch? Well, yes. You might get a phone call that distracts you. Well, I can talk on the way down. You might trip on your way out the door and break your leg and now you're not going to go down there. I say, yes, but that's not going to happen, but it could. And notice all of a sudden probabilities come back into play. So it is not the case I will absolutely meet you at 12 o'clock for lunch. There's a 99.9% chance I will, but it's probabilistic. It's not a certainty. This tendency to think in absolutes causes us to miss some important things when it comes to probabilities. For example, people on average tend to be far more afraid of flying than driving. So you see on the news an airplane crash and you think, well, these are dangerous things, I need to be careful flying. What you tend not to see on the news are car crashes. Now, if it's a particularly bad one, you'll see it, right? But generally you tend not to see them. And so when we think about probabilities, we associate flying with some probability of mishap, but we don't associate driving with the probability of mishap. We think of driving much more in absolute terms of, yes, well, I'm a careful driver, therefore nothing will happen. And what we miss in thinking in terms of absolutes is the opportunity to weigh these two things that are both of which are probabilistic. If you look at the numbers, in 2013, 35 people in the United States died in commercial plane crashes. That same year, 34,000 people died in car crashes. And interestingly, the reason that you see the plane crashes on the television and not the car crashes is because they're rare. The point of the media is to make money selling advertising, right? Of course they're going to inform you, but the way they stay in business is by selling advertising and the way they sell advertising is to get your attention, get you watching. And the way I get you watching is by showing you things that are uncommon. So interestingly, seen in that light, in a probabilistic light, the news is really a list of things that you don't have to worry about harming you. It's the things that don't appear on the news that are likely to harm you. So is probability? I mean, it sounds like sort of like there's an intersection between probability or maybe a mix up between probability and like textbooks, psychology, you know, because it sounds like a lot of heuristic models, you know, things like anchoring where, you know, you'll say, OK, well, this person's age might be between 15 and 75. And we sort of use that as an anchor and say, well, it's more likely to be a little older than we might expect. So it seems like, you know, in the way that you're describing of that, you know, a lot of what we consider to be probability is just some sort of heuristic that skews data. Yeah, absolutely. And I would say it's not it's not that the heuristic is skewing the data. It's it's skewing our perception of what's going on. Good case in point. This is an emotional reaction when people talk about assault rifles. Of course, the word itself is, you know, jarring. And so you you immediately associate it with badness. And these are things that are going to harm people. We need to get rid of them. Following your gut like that, you're relying on these these mental heuristics, which are which are are influenced by your your natural biases and emotions and this sort of thing. Things have nothing to do with probabilities. If you look at the statistics, the probabilities, right, the number of people who are killed in the United States by fists and feet. Is twice the number that are killed by rifles or rifles combined, not just assault rifles. And yet we don't have serious conversations about banning fists and feet. Now, now notice what's happening here. We have a confluence of of emotion and fact. And one of the things that that people tend to do that we have to guard against is is thinking that the facts somehow are are heartless. They're not human, right? So if I say, well, let's talk about the probabilities of death. Immediately, people start to shut down. This guy doesn't care about human life. It's not that at all. If you're going to make a choice for society, we should ban this or ban that or we should invest in this or not invest in or everybody should have this product or not have this product, whatever, whatever policy it is we're going to have for society. You've got to look at the probabilities because you're going to impose a rule on everybody. The rule will apply well some places and poorly others and kind of OK in other places. But what's important is what happens on average, because we're applying the rules to everybody. What happens on average to this society when we apply this rule? And that requires looking at it coldly at the probabilities and say, look, the probability of being killed by rifle in the United States is one half the probability of being killed by feet and fists. So that's not to say that we should be unconcerned with rifles, but it is to say we should be half as concerned about them as we are about fists and feet. So one problem that we encounter with with probabilities is this is this emotional attachment that we have and in the bias of seeing things on television versus not. Another problem we have with with probabilities is we tend to we tend to regard things that we hear repeatedly as being more true. Give you some statistics to look at. So we hear the phrase, the rich need to pay their fair share. Talking about taxes. And much of this conversation when you hear people talk about the rich paying their fair share, invariably, someone will will pick, you know, someone be at Mitt Romney or someone else who makes multi tens of millions of dollars and who apparently paid very little taxes and we say, we'll look at this person. Right. And what we've done is are two things. One, we've repeated something we hear that the rich don't pay their fair share and we point to an anecdote, a specific example. And that combination of repeating this thing and showing an example that appears to support this claim that we're making kind of starts to encode this claim in some order of truth. And when we start to to encode the claim in an order of truth, we miss the opportunity to actually look at the numbers and see what the truth is. What are the actual figures for the rich versus the poor? So this becomes very interesting if you look at data for the United States and in a beautiful twist of cosmic karma, you can find this data on the White House website. And what you're seeing here is the average effective income tax rate for various income categories in the US. These numbers, of course, fluctuate year to year. This is the latest year that there's data available for, which I believe is 2011, maybe 2012. But the data year to year is does not change much from what you see here. And what you're seeing is first off, we're talking about the average effective income tax rate. So what that means is it's not the statutory rate, the rate that by law you're required to pay. Rather, it's, we find this number by taking all of the money that you pay to the IRS. So after you do your accounting and legal gymnastics and you have write offs and exemptions and offshore, whatever it is that you have, let all the dust settle and answer two questions. First, how much money did you pay to the IRS? And second, what's your income? The first divided by the second is this. This is the average effective income tax rate. And what you see is now remember their exceptions, right? This is the average for each group. The average American amongst the poorest 20% of Americans pays about 2% of his income in taxes. Middle Americans, middle income Americans pay about 11%. In the top 1% pay almost 30% of their income in taxes. Now, you could argue whether or not this is fair. And that's an interesting argument to have. But to start the discussion of what is fair, you've got to start from the statistics that currently exist. And this is what we, this is the current state of affairs. So one of the reasons that we, we have this perception, many people have this perception that the rich don't pay their fair share is because we repeat it. You hear it and it must be true because everybody says it. And so I say it and I become one of the people who is everybody who's saying it. And before you know it, this has taken on a life of its own. So with probabilities, be care about observation bias. The stuff you see on the news isn't is likely not to hurt you. It's the stuff you don't see that will be careful about repetition. Things that you hear repeated aren't necessarily true simply because they're repeated. Third, be very careful about using your heart and good intentions to address problems that require the use of statistics. Case in point, in 2001, shortly after 9-11 actually, a teenager in Florida flew a Cezna plane into a building killing himself. There was, you know, many questions about what was going on. The authorities relatively quickly determined this was not a terrorist event. But after some digging, it was discovered that this teenager was on a prescription drug. And there were many other teenagers throughout the country who had similarly committed suicide, who were on this prescription drug. And so this led to a call following, you know, our feelings and our hearts, our good intentions, a call to the FDA to ban this drug. And notice what happens here. You feel for the kid, you feel for the parents, you feel for all the parents of the other kids who have committed suicide and you want to do something. And clearly there's this connection and you'll hear things like people say, well, wait a minute, let's look at the numbers and see if really this drug is causing, is causing the suicide and you'll hear a response. It doesn't matter if it saves just one life, we should ban this drug. So the argument you get in response is almost one of the statistics don't matter because life trumps them. Even if banning this drug doesn't save them, it might save one of them. If it saves one of them, it's worth all the effort. Fine, let's go and ban it. Well, here's, here's what happens when we apply statistics to the, to the event. If you compare the number of teenagers in 2001 who were taking this prescription drug to the number who weren't and the number of teenagers in 2001 who committed suicide and those that didn't, you cross reference the two data sets, what you find is that the probability of a teenager committing suicide when they're on the drug is about one tenth what the probability is of them committing suicide when they're not on the drug. That is, when you put aside your good intentions and your heart for a moment and look, someone say heartlessly at the data, look logically at the data, what you find is that the drug was actually contributing to a lower suicide rate. And why is this the case? Well, it turns out the drug in question was one for severe acne. And of course, being teenagers and you know, you're concerned about, you know, do you fit in with your peers and so on and so forth and acne can is a negative contributor to fitting in with others and being kind of, you know, what people teenagers think is normal. The drug was helping this problem and thereby reducing the teenage suicide rate. We don't see that unless we step back and take a deep breath and think about the statistics as opposed to thinking about our good intentions. We came very close as a country to banning a drug far from saving at least one life, banning the drug would have cost teenage lives.