Statistical Motivational Quotes

In the business people with expertise, experience and evidence will make more profitable decisions than people with instinct, intuition and imagination.

According to Google statistics, people search the word "money" four times as often as the word "goal". This creates a word: "frustration".

Statistics show that the nature of English crime is reverting to its oldest habits. In a country where so many desire status and wealth, petty annoyances can spark disproportionately violent behaviour. We become frustrated because we feel powerless, invisible, unheard. We crave celebrity, but that’s not easy to come by, so we settle for notoriety. Envy and bitterness drive a new breed of lawbreakers, replacing the old motives of poverty and the need for escape. But how do you solve crimes which no longer have traditional motives?

In my opinion, defining intelligence is much like defining beauty, and I don’t mean that it’s in the eye of the beholder. To illustrate, let’s say that you are the only beholder, and your word is final. Would you be able to choose the 1000 most beautiful women in the country? And if that sounds impossible, consider this: Say you’re now looking at your picks. Could you compare them to each other and say which one is more beautiful? For example, who is more beautiful— Katie Holmes or Angelina Jolie? How about Angelina Jolie or Catherine Zeta-Jones? I think intelligence is like this. So many factors are involved that attempts to measure it are useless. Not that IQ tests are useless. Far from it. Good tests work: They measure a variety of mental abilities, and the best tests do it well. But they don’t measure intelligence itself.

Why figure out what will motivate this kid to learn if, statistically, he’s just another young Black male destined to drop out anyway? He was never referred to a counselor, never tested for a learning disorder, never given some sort of individual attention that might better equip him to navigate junior high school and high school.

Psychologists call this “motivated reasoning.” Motivated reasoning is thinking through a topic with the aim, conscious or unconscious, of reaching a particular kind of conclusion. In a football game, we see the fouls committed by the other team but overlook the sins of our own side. We are more likely to notice what we want to notice.11

Unexpectedly, we found that the factors most people usually think of as driving group performance—i.e., cohesion, motivation, and satisfaction—were not statistically significant. The largest factor in predicting group intelligence was the equality of conversational turn taking; groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn taking. The second most important factor was the social intelligence of a group’s members, as measured by their ability to read each other’s social signals. Women tend to do better at reading social signals, so groups with more women tended to do better (see the Social Signals Special Topic Box [at the end of Chapter 7]).

One of the most telling statistics regarding multiples is that 97 percent of them have had a history of severe childhood trauma, often in the form of monstrous psychological, physical, and sexual abuse. This has led many researchers to conclude that becoming a multiple is the psyche's way of coping with extraordinary and soul-crushing pain. By dividing up into one or more personalities the psyche is able to parcel out the pain, in a way, and have several personalities bear what would be too much for just one personality to withstand. In this sense becoming a multiple may be the ultimate example of what Bohm means by fragmentation. It is interesting to note that when the psyche fragments itself, it does not become a collection of broken and jagged-edged shards, but a collection of smaller wholes, complete and self-sustaining with their own traits, motives, and desires. Although these wholes are not identical copies of the original personality, they are related to the dynamics of the original personality, and this in itself suggests that some kind of holographic process is involved.

For the psychologist Paul Bloom, this is a huge downside. Empathy, he argues, focuses our attention on single individuals, leading us to become both parochial and insensitive to scale.62 As Bertrand Russell is often reported to have said, “The mark of a civilized man is the capacity to read a column of numbers and weep,”63 but few of us are capable of truly feeling statistics in this way. If only we could be moved more by our heads than our hearts, we could do a lot more good. And yet the incentives to show empathy and spontaneous compassion are overwhelming. Think about it: Which kind of people are likely to make better friends, coworkers, and spouses—“calculators” who manage their generosity with a spreadsheet, or “emoters” who simply can’t help being moved to help people right in front of them? Sensing that emoters, rather than calculators, are generally preferred as allies, our brains are keen to advertise that we are emoters. Spontaneous generosity may not be the most effective way to improve human welfare on a global scale, but it’s effective where our ancestors needed it to be: at finding mates and building a strong network of allies.

Some people are bound to be average. Isn’t that a statistical certainty?” “My understanding of what it means to be average has nothing to do with statistics. The average American is truly average only in the ways he falls short of his own potential, particularly when he is motivated by the expectations of others. There is always someone more to the right or left of you, someone more or less attractive than you, someone richer or poorer, someone who claims to know how you should live your life better than you know it. People are average when they are driven by a motivation to fit in. The American challenge, then, is to be oneself—only, exactly, and totally.

According to a 2000 New York Times study of 100 "rampage" mass murders, where 425 people were killed and 510 injured, the killers: 1. Often have serious mental health issues 2. Are not usually motivated by exposure to videos, movies, or television 3. Are not using alcohol or other drugs at the time of the attacks 4. Are often unemployed 5. Are sometimes female 6. Are not usually Satanists or racists 7. Are most often white males although a few are Asian or African American 8. Sometimes have college degrees or some years of college 9. Often have military experience 10. Give lots of pre-attack warning signals 11. Often carry semiautomatic weapons obtained legally 12. Often do no attempt escape 13. Half commit suicide or are killed by others 14. Most have a death wish (Fessenden, 2000)

I would advise those who think that self-help is the answer to familiarize themselves with the long history of such efforts in the Negro community, and to consider why so many foundered on the shoals of ghetto life. It goes without saying that any effort to combat demoralization and apathy is desirable, but we must understand that demoralization in the Negro community is largely a common-sense response to an objective reality. Negro youths have no need of statistics to perceive, fairly accurately, what their odds are in American society. Indeed, from the point of view of motivation, some of the healthiest Negro youngsters I know are juvenile delinquents. Vigorously pursuing the American dream of material acquisition and status, yet finding the conventional means of attaining it blocked off, they do not yield to defeatism but resort to illegal (and often ingenious) methods.... If Negroes are to be persuaded that the conventional path (school, work, etc.) is superior, we had better provide evidence which is now sorely lacking.

ON THE MODUS OPERANDI OF OUR CURRENT PRESIDENT, DONALD J. TRUMP "According to a new ABC/Washington Post poll, President Trump’s disapproval rating has hit a new high." The President's response to this news was "“I don’t do it for the polls. Honestly — people won’t necessarily agree with this — I do nothing for the polls,” the president told reporters on Wednesday. “I do it to do what’s right. I’m here for an extended period of time. I’m here for a period that’s a very important period of time. And we are straightening out this country.” - Both Quotes Taken From Aol News - August 31, 2018 In The United States, as in other Republics, the two main categories of Presidential motivation for their assigned tasks are #1: Self Interest in seeking to attain and to hold on to political power for their own sakes, regarding the welfare of This Republic to be of secondary importance. #2: Seeking to attain and to hold on to the power of that same office for the selfless sake of this Republic's welfare, irregardless of their personal interest, and in the best of cases going against their personal interests to do what is best for this Republic even if it means making profound and extreme personal sacrifices. Abraham Lincoln understood this last mentioned motivation and gave his life for it. The primary information any political scientist needs to ascertain regarding the diagnosis of a particular President's modus operandi is to first take an insightful and detailed look at the individual's past. The litmus test always being what would he or she be willing to sacrifice for the Nation. In the case of our current President, Donald John Trump, he abandoned a life of liberal luxury linked to self imposed limited responsibilities for an intensely grueling, veritably non stop two year nightmare of criss crossing this immense Country's varied terrain, both literally and socially when he could have easily maintained his life of liberal leisure. While my assertion that his personal choice was, in my view, sacrificially done for the sake of a great power in a state of rapid decline can be contradicted by saying it was motivated by selfish reasons, all evidence points to the contrary. For knowing the human condition, fraught with a plentitude of weaknesses, for a man in the end portion of his lifetime to sacrifice an easy life for a hard working incessant schedule of thankless tasks it is entirely doubtful that this choice was made devoid of a special and even exalted inspiration to do so. And while the right motivations are pivotal to a President's success, what is also obviously needed are generic and specific political, military and ministerial skills which must be naturally endowed by Our Creator upon the particular President elected for the purposes of advancing a Nation's general well being for one and all. If one looks at the latest National statistics since President Trump took office, (such as our rising GNP, the booming market, the dramatically shrinking unemployment rate, and the overall positive emotive strains in regards to our Nation's future, on both the left and the right) one can make definitive objective conclusions pertaining to the exceptionally noble character and efficiency of the current resident at 1600 Pennsylvania Avenue. And if one can drown out the constant communicative assaults on our current Commander In Chief, and especially if one can honestly assess the remarkable lack of substantial mistakes made by the current President, all of these factors point to a leader who is impressively strong, morally and in other imperative ways. And at the most propitious time. For the main reason that so many people in our Republic palpably despise our current President is that his political and especially his social agenda directly threatens their licentious way of life. - John Lars Zwerenz

Christians often fail to get in touch with the shocking message that can lie at the heart of evangelism: “I am here to change you, and I’m going to change you so that you become like me.” There are some obvious dangers here once we think about all this. If we approach people in this way, we are not treating them as people. We are not respecting them. We are treating them as part of our own program, like an objective and a statistic, and this is self-centered as well as disrespectful. An obnoxious smell of superiority is apparent. Further, we are judging people as fundamentally inadequate. *We* are okay, of course. Missionary work conducted in this spirit is a well-intentioned but self-centered power-play… We can avoid this instrumentalizing of potential converts - a making of them into something like an instrument or tool that then does something for us - only by approaching them for their own sakes and hence not as potential converts at all. We must value our initial relationships with people for what they are and not in terms of what we want out of them. This means that we must want to become their friends. Moreover, it must be a friendship with no strings attached. We must seek out relationships because we are interested in and value other people for who they are, right where they are. Conversions would be nice, but they are not our main agenda. We hope and pray for the best for our new friends, but that is not our principal motivation for relating to them. In this way and only in this way do we avoid colonizing people as we convert them.

Recognize that your own motivation, ambition, and talents will determine your success more than the college name on your diploma.”8

Ida was clearly exasperated by the fact that despite the motives that accompanied the lynching statistics published year after year—which Ida included in nearly every article—“law-abiding and fair-minded people should so persistently shut their eyes to the facts.” Ida continued, “This record, easily within the reach of every one who wants it,” made it “inexcusable” for anyone not to debunk the presumption from the beginning.

Most motivational speakers typically use anecdotes rather than statistics to prove their point. They don’t speak about the thousands of failures of millions of hapless men and women but always tell the story of one successful person to prove their point. They make great promises akin to a good salesman out to sell his wares. Many people follow their advice and unfortunately discover that they still are not able to achieve the promised success.

It’s important to think of what it means to powerfully and successfully rise to challenges, and all that it requires: determination, clarity, and steering clear of victim mentality

We can pontificate all day about statistics and percentages and death tolls and displacement numbers, easily forgetting that numbers have names and names have faces. Each face has wrinkles that make up a map filled with tragedies to tell.

Thus, multiple regression requires two important tasks: (1) specification of independent variables and (2) testing of the error term. An important difference between simple regression and multiple regression is the interpretation of the regression coefficients in multiple regression (b1, b2, b3, …) in the preceding multiple regression model. Although multiple regression produces the same basic statistics discussed in Chapter 14 (see Table 14.1), each of the regression coefficients is interpreted as its effect on the dependent variable, controlled for the effects of all of the other independent variables included in the regression. This phrase is used frequently when explaining multiple regression results. In our example, the regression coefficient b1 shows the effect of x1 on y, controlled for all other variables included in the model. Regression coefficient b2 shows the effect of x2 on y, also controlled for all other variables in the model, including x1. Multiple regression is indeed an important and relatively simple way of taking control variables into account (and much easier than the approach shown in Appendix 10.1). Key Point The regression coefficient is the effect on the dependent variable, controlled for all other independent variables in the model. Note also that the model given here is very different from estimating separate simple regression models for each of the independent variables. The regression coefficients in simple regression do not control for other independent variables, because they are not in the model. The word independent also means that each independent variable should be relatively unaffected by other independent variables in the model. To ensure that independent variables are indeed independent, it is useful to think of the distinctively different types (or categories) of factors that affect a dependent variable. This was the approach taken in the preceding example. There is also a statistical reason for ensuring that independent variables are as independent as possible. When two independent variables are highly correlated with each other (r2 > .60), it sometimes becomes statistically impossible to distinguish the effect of each independent variable on the dependent variable, controlled for the other. The variables are statistically too similar to discern disparate effects. This problem is called multicollinearity and is discussed later in this chapter. This problem is avoided by choosing independent variables that are not highly correlated with each other. A WORKING EXAMPLE Previously (see Chapter 14), the management analyst with the Department of Defense found a statistically significant relationship between teamwork and perceived facility productivity (p <.01). The analyst now wishes to examine whether the impact of teamwork on productivity is robust when controlled for other factors that also affect productivity. This interest is heightened by the low R-square (R2 = 0.074) in Table 14.1, suggesting a weak relationship between teamwork and perceived productivity. A multiple regression model is specified to include the effects of other factors that affect perceived productivity. Thinking about other categories of variables that could affect productivity, the analyst hypothesizes the following: (1) the extent to which employees have adequate technical knowledge to do their jobs, (2) perceptions of having adequate authority to do one’s job well (for example, decision-making flexibility), (3) perceptions that rewards and recognition are distributed fairly (always important for motivation), and (4) the number of sick days. Various items from the employee survey are used to measure these concepts (as discussed in the workbook documentation for the Productivity dataset). After including these factors as additional independent variables, the result shown in Table 15.1 is

regression line will have larger standard deviations and, hence, larger standard errors. The computer calculates the slope, intercept, standard error of the slope, and the level at which the slope is statistically significant. Key Point The significance of the slope tests the relationship. Consider the following example. A management analyst with the Department of Defense wishes to evaluate the impact of teamwork on the productivity of naval shipyard repair facilities. Although all shipyards are required to use teamwork management strategies, these strategies are assumed to vary in practice. Coincidentally, a recently implemented employee survey asked about the perceived use and effectiveness of teamwork. These items have been aggregated into a single index variable that measures teamwork. Employees were also asked questions about perceived performance, as measured by productivity, customer orientation, planning and scheduling, and employee motivation. These items were combined into an index measure of work productivity. Both index measures are continuous variables. The analyst wants to know whether a relationship exists between perceived productivity and teamwork. Table 14.1 shows the computer output obtained from a simple regression. The slope, b, is 0.223; the slope coefficient of teamwork is positive; and the slope is significant at the 1 percent level. Thus, perceptions of teamwork are positively associated with productivity. The t-test statistic, 5.053, is calculated as 0.223/0.044 (rounding errors explain the difference from the printed value of t). Other statistics shown in Table 14.1 are discussed below. The appropriate notation for this relationship is shown below. Either the t-test statistic or the standard error should be shown in parentheses, directly below the regression coefficient; analysts should state which statistic is shown. Here, we show the t-test statistic:3 The level of significance of the regression coefficient is indicated with asterisks, which conforms to the p-value legend that should also be shown. Typically, two asterisks are used to indicate a 1 percent level of significance, one asterisk for a 5 percent level of significance, and no asterisk for coefficients that are insignificant.4 Table 14.1 Simple Regression Output Note: SEE = standard error of the estimate; SE = standard error; Sig. = significance.

IN 1930, JOSEPH ROSSMAN, who had served for decades as an examiner in the U.S. Patent Office, polled more than seven hundred patentees, producing a remarkable picture of the mind of the inventor. Some of the results were predictable;6 the three biggest motivators were “love of inventing,” “desire to improve,” and “financial gain,” the ranking for each of which was statistically identical, and each at least twice as important as those appearing down the list, such as “desire to achieve,” “prestige,” or “altruism” (and certainly not the old saw, “laziness,” which was named roughly one-thirtieth as frequently as “financial gain”). A century after Rocket, the world of technology had changed immensely: electric power, automobiles, telephones. But the motivations of individual inventors were indistinguishable from those inaugurated by the Industrial Revolution.

The social-personality approach to studying creativity focuses on personality and motivational variables as well as the socio-cultural environment as sources of creativity. Sternberg (2000) states that numerous studies conducted at the societal level indicate that “eminent levels of creativity over large spans of time are statistically linked to variables such as cultural diversity, war, availability of role models, availability of financial support, and competitors in a domain” (p. 9).

Speaking on Stage Speakers and presenters have only a few short seconds before their audience members begin forming opinions. True professionals know that beginning with impact determines audience engagement, the energy in the room, positive feedback, the quality of the experience, and whether or not their performance will be a success. A few of the popular methods which you can use to break the ice from the stage are: • Using music. • Using quotes. • Telling a joke. • Citing statistics. • Showing a video. • Asking questions. • Stating a problem. • Sharing acronyms. • Sharing a personal story. • Laying down a challenge. • Using analogies and comparisons. • Taking surveys; raise your hand if . . . Once you refine, define, and discover great conversation starters, you will enjoy renewed confidence for communicating well with new people.

Employee Engagement “Employee Engagement” has become a very hot topic in recent years. The escalating statistics for disengagement are alarming. In 2015, the Gallup Polls’ “The State of the American Workforce” survey found that only 32.5 percent of the U.S. Workforce is engaged and committed where they work, and 54 percent say they would consider leaving their companies if they could receive a 20 percent raise elsewhere. Disengagement not only lowers performance, morale, and productivity, but it’s costing employers billions of dollars a year. It's a growing problem, which has many companies baffled.

There is nothing better than to have a highly motivated team of leaders focused on than reaching those far from Christ. And yet statistics and our experience reveal that evangelism entropy can creep deep inside a new church within months of its first public service. The longer we are around new churches the more amazed we are at how quickly these mission-focused, vibrant new churches become old.

in order to attract private actors to carry through their innovation projects and policies, various components of the NSS have to create, and periodically update, a whole system of incentives and organizational arrangements—ranging from the funding and design of technology development to intellectual property and procurement reforms. Over time, this motivating process draws the NSS further and further into promoting commercial technology from which both sectors can draw benefit. But throughout this process of give and take, the NSS continues to set the goals, make the rules (for example, by setting performance standards), and define the problem sets for industry and university researchers to tackle. The outcome is what I characterize as a system of governed interdependence—neither “statist” nor “free-market” in its approach to inducing transformative innovation.

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Operational deficiencies are made visible through statistical analysis, and likewise growth opportunities.

Thus, important parts of physics and chemistry have been pressed into the service of war and destruction; much mathematical and statistical ingenuity has been turned into an auxiliary of monopolistic market control and profit maximization; psychology has become a prostitute of 'motivation research' and personnel management; biology is made into a handmaiden of pharmaceutical rackets; and art, language, color, and sound have been degraded into instrumentalities of advertising.

But I need you all, my dear students, to speak beauty more than figures, speak phrases of encouragement more than precise mathematical statistics, speak words of innovation more than historical events and you should speak with your soul rather than just for the sake. What good is it to be famous, if you can’t speak well? So, by the end of this year, we shall have many motivational speakers, and all of us will live a motivated life. Speaking is an art, and everyone cannot become an artist. But give in your heart and soul, and nothing is impossible.

Three of the leading opponents of behavioral genetics collaborated on a book that set out to deconstruct the new science and reverse the biological tide. The book was Not in Our Genes, and the authors were three of the most vigilant critics of the genetic view: Richard Lewontin, a population geneticist at Harvard; the indefatigable Leon Kamin, who was then at Princeton’s psychology department; and Steven Rose, a neurobiologist at England’s Open University. Although the book had slight impact, it is worth examining as a compendium of the arguments and methods of the opponents of behavioral genetics, arguments that these critics, and their shrinking band of allies, continue to make despite repeated refutations. Throughout the text the authors, with admirable candor, proclaim their Marxist perspective and their “commitment to … a more socially just—a socialist—society.” Few pages go by without references to “dialectics,” “bourgeois society,” and “capitalist values.” The authors’ apparently feel their clean breast about their politics permitted wholesale assumptions about those of their opponents. We are leftists is their implicit claim; but you on the other side of the scientific fence are reactionaries. Liberals, they appeared to be saying, can have only one scientific view, theirs; any other must be right-wing and antiliberal. “Biological determinist ideas,” they say, “are part of the attempt to preserve the inequalities of our society and to shape human nature in its own image.” It must surely have come as unpleasant news to Sandra Scarr, Jerome Kagan, and other liberal psychologists to learn that they were striving to preserve society’s inequalities. In addition, the authors’ nasty assumptions of their opponents’ motives must have been an eye-opener to the hundreds of microbiologists, lab technicians, DNA scanners, rat-runners, statistical analysts, and all the others engaged in behavioral genetics research who learned from the book that they were going to work each day “to preserve the interests of the dominant class, gender, and race.” But the falsity of the authors’ premise goes well beyond slandering a few individuals. Throughout the text, the writers deny the possibility that scientists could exist who place their curiosity about the world ahead of their political agendas. Lewontin, Kamin, and Rose deny as well the possibility of any man or woman, including themselves, separating science from politics. (“Science is not and cannot be above ‘mere’ politics.”) They leave no room for the scientist who is so intrigued by new information, in this case gene-behavior discoveries, that he or she is oblivious to alleged political consequences. For the authors, all scientists who seek out biological influences on behavior, from Darwin to Robert Plomin, are willing servants of the status quo, if not promoters of a return to feudalism.

Statistically speaking, you’re modeling the marriage your children will have.

Statistically, the incidence of ‘true statements’—definitional, demonstrative, tautological—in any given mass of discourse is probably small. The current of language is intentional, it is instinct with purpose in regard to audience and situation. It aims at attitude and assent. It will, except on specialized occasions of logically formal, prescriptive, or solemnized utterance, not convey ‘truth’ or ‘information of facts’ at all. We communicate motivated images, local frameworks of feeling. All descriptions are partial. We speak less than the truth, we fragment in order to reconstruct desired alternatives, we select and elide.

Believe you are not part of the statistics because you are inspired and willing to put in as much effort as is required and because you want to be in the driving seat of your life, become the absolute captain of it, and direct it toward beautiful, adventurous, and fulfilling horizons.

the title of this chapter, “Recognize that your own motivation, ambition, and talents will determine your success more than the college name on your diploma.

Now only 20 percent of Norwegian prisoners are arrested in the two years after their release. In contrast, a recent Bureau of Justice Statistics report found that 44 percent of American state prisoners were arrested at least once in the first year after their release. Høidal attributes the Norwegian turnaround directly to the change in officers’ responsibilities. Each officer is designated as the “contact officer” for three inmates. The officer is assigned responsibility for these three inmates for the duration of their incarceration. “They’re like a coach, motivator, role model,” Høidal says of the contact officers. And “like a nurse when you’re in the hospital,” they are familiar with your case; they’re the person to whom you reach out first when you need something, whether that something is help in beginning a job, or classes in the prison, or finding housing or work in the community for the future.

motivational interviewing has a statistically and clinically meaningful effect on behavior change in roughly three out of four studies, and psychologists and physicians using it have a success rate of four in five. There aren’t many practical theories in the behavioral sciences with a body of evidence this robust.

In either case, we now have a good reason to believe that the difference we observed in the experiment was probably not due to chance. This is referred to in technical jargon with a confusing double negative: “rejection of the null hypothesis.” To determine exactly what caused the difference we observed requires much more research, but at the initial stage when we just want to know if there are any differences at all, this outcome provides ample motivation to keep on investigating. In particular, even if an experiment produces extremely high odds against chance, this doesn’t mean that the effect we’re interested in is proven. All the annoying cautions and qualifications commonly used in scientific lingo—it might be this, it could possibly be that, the purported results may perhaps be such and such—sound like a curious lack of enthusiasm, or an unwillingness to take a firm stand. But the prudence is intentional. It prevents existing knowledge from coagulating into unshakable dogma, which is the forte of religious faith. Also, just because a statistical test ends up with huge odds against chance doesn’t necessarily mean that the effect we were measuring is what we imagined it to be. To gain that sort of confidence it takes many independent scientists repeatedly examining the same effect in different ways, and for the results to be consistent on average.

YouTube: "Jordan Peterson | The Most Terrifying IQ Statistic" JORDAN PETERSON: One of the most terrifying statistics I ever came across was one detailing out the rationale of the United States Armed Forces for not allowing the induct … you can't induct anyone into the Armed Forces into the Armed Forces in the U.S. if they have an IQ of less than 83. Okay, so let's just take that apart for a minute, because it's a horrifying thing. So, the U.S. Armed Forces have been in the forefront of intelligence research since World War I because they were onboard early with the idea that, especially during war time when you are ramping up quickly that you need to sort people effectively and essentially without prejudice so that you can build up the officer corps so you don't lose the damned war, okay. So, there is real motivation to get it right, because it's a life-and-death issue, so they used IQ. They did a lot of the early psychometric work on IQ. Okay, so that's the first thing, they are motivated to find an accurate predictor, so they settled on IQ. The second thing was, the United States Armed Forces is also really motivated to get people into the Armed Forces, peacetime or wartime. Wartime, well, for obvious reasons. Peacetime, because, well, first of all you've got to keep the Armed Forces going and second you can use the Armed Forces during peacetime as a way of taking people out of the underclass and moving them up into the working class or the middle class, right. You can use it as a training mechanism, and so left and right can agree on that, you know. It's a reasonable way of promoting social mobility. So again, the Armed Forces even in peacetime is very motivated to get as many people in as they possibly can. And it's difficult as well. It's not that easy to recruit people, so you don't want to throw people out if you don't have to. So, what's the upshot of all that? Well, after one hundred years, essentially, of careful statistical analysis, the Armed Forces concluded that if you had an IQ of 83 or less there wasn't anything you could possibly be trained to do in the military at any level of the organization that wasn't positively counterproductive. Okay, you think, well, so what, 83, okay. Yeah, one in ten! One in ten! That's one in ten people! And what that really means, as far as I can tell, is if you imagine that the military is approximately as complex as the broader society, which I think is a reasonable proposition, then there is no place in our cognitively complex society for one in ten people. So what are we going to do about that? The answer is, no one knows. You say, "well, shovel money down the hierarchy." It's like, the problem isn't lack of money. I mean sometimes that's the problem, but the problem is rarely absolute poverty. It's rarely that. It is sometimes, but rarely. It's not that easy to move money down the hierarchy. So, first of all, it's not that easy to manage money. So, it's a vicious problem, man. And so... INTERVIEWER: It's hard to train people to become creative, adaptive problem solvers. PETERSON: It's impossible! You can't do it! You can't do it! You can interfere with their cognitive ability, but you can't do that! The training doesn't work. INTERVIEWER: It's not going to work in six months, but it could have worked in six years. PETERSON: No, it doesn't work. Sorry, it doesn't work. The data on that is crystal clear. [note that “one in ten” applies to a breeding group with an average IQ of 100]