Percentage Difference Between Two Quotes

Suppose you were to total up all the wars over the past two hundred years that occurred between very large and very small countries. Let’s say that one side has to be at least ten times larger in population and armed might than the other. How often do you think the bigger side wins? Most of us, I think, would put that number at close to 100 percent. A tenfold difference is a lot. But the actual answer may surprise you. When the political scientist Ivan Arreguin-Toft did the calculation a few years ago, what he came up with was 71.5 percent. Just under a third of the time, the weaker country wins. Arreguin-Toft then asked the question slightly differently. What happens in wars between the strong and the weak when the weak side […] refuses to fight the way the bigger side wants to fight, using unconventional or guerilla tactics? The answer: in those cases, the weaker party’s winning percentage climbs from 28.5 percent to 63.6 percent. To put that in perspective, the United Stats’ population is ten times the size of Canada’s. If the two countries went to war and Canada chose to fight unconventionally, history would suggest that you ought to put your money on Canada.

Working hard is important. But more effort does not necessarily yield more results. “Less but better” does. Ferran Adrià, arguably the world’s greatest chef, who has led El Bulli to become the world’s most famous restaurant, epitomizes the principle of “less but better” in at least two ways. First, his specialty is reducing traditional dishes to their absolute essence and then re-imagining them in ways people have never thought of before. Second, while El Bulli has somewhere in the range of 2 million requests for dinner reservations each year, it serves only fifty people per night and closes for six months of the year. In fact, at the time of writing, Ferran had stopped serving food altogether and had instead turned El Bulli into a full-time food laboratory of sorts where he was continuing to pursue nothing but the essence of his craft.1 Getting used to the idea of “less but better” may prove harder than it sounds, especially when we have been rewarded in the past for doing more … and more and more. Yet at a certain point, more effort causes our progress to plateau and even stall. It’s true that the idea of a direct correlation between results and effort is appealing. It seems fair. Yet research across many fields paints a very different picture. Most people have heard of the “Pareto Principle,” the idea, introduced as far back as the 1790s by Vilfredo Pareto, that 20 percent of our efforts produce 80 percent of results. Much later, in 1951, in his Quality-Control Handbook, Joseph Moses Juran, one of the fathers of the quality movement, expanded on this idea and called it “the Law of the Vital Few.”2 His observation was that you could massively improve the quality of a product by resolving a tiny fraction of the problems. He found a willing test audience for this idea in Japan, which at the time had developed a rather poor reputation for producing low-cost, low-quality goods. By adopting a process in which a high percentage of effort and attention was channeled toward improving just those few things that were truly vital, he made the phrase “made in Japan” take on a totally new meaning. And gradually, the quality revolution led to Japan’s rise as a global economic power.3

Suppose you were to total up all the wars over the past two hundred years that occurred between very large and very small countries. Let’s say that one side has to be at least ten times larger in population and armed might than the other. How often do you think the bigger side wins? Most of us, I think, would put that number at close to 100 percent. A tenfold difference is a lot. But the actual answer may surprise you. When the political scientist Ivan Arreguín-Toft did the calculation a few years ago, what he came up with was 71.5 percent. Just under a third of the time, the weaker country wins. Arreguín-Toft then asked the question slightly differently. What happens in wars between the strong and the weak when the weak side does as David did and refuses to fight the way the bigger side wants to fight, using unconventional or guerrilla tactics? The answer: in those cases, the weaker party’s winning percentage climbs from 28.5 percent to 63.6 percent. To put that in perspective, the United States’ population is ten times the size of Canada’s. If the two countries went to war and Canada chose to fight unconventionally, history would suggest that you ought to put your money on Canada.

We already have eight hundred million people living in hunger—and population is growing by eighty million a year. Over a billion people are in poverty—and present industrial strategies are making them poorer, not richer. The percentage of old people will double by 2050—and already there aren’t enough young people to care for them. Cancer rates are projected to increase by seventy percent in the next fifteen years. Within two decades our oceans will contain more microplastics than fish. Fossil fuels will run out before the end of the century. Do you have an answer to those problems? Because I do. Robot farmers will increase food production twentyfold. Robot carers will give our seniors a dignified old age. Robot divers will clear up the mess humans have made of our seas. And so on, and so on—but every single step has to be costed and paid for by the profits of the last.” He paused for breath, then went on, “My vision is a society where autonomous, intelligent bots are as commonplace as computers are now. Think about that—how different our world could be. A world where disease, hunger, manufacturing, design, are all taken care of by AI. That’s the revolution we’re shooting for. The shopbots get us to the next level, that’s all. And you know what? This is not some binary choice between idealism or realism, because for some of us idealism is just long-range realism. This shit has to happen. And you need to ask yourself, do you want to be part of that change? Or do you want to stand on the sidelines and bitch about the details?” We had all heard this speech, or some version of it, either in our job interviews, or at company events, or in passionate late-night tirades. And on every single one of us it had had a deep and transformative effect. Most of us had come to Silicon Valley back in those heady days when it seemed a new generation finally had the tools and the intelligence to change the world. The hippies had tried and failed; the yuppies and bankers had had their turn. Now it was down to us techies. We were fired up, we were zealous, we felt the nobility of our calling…only to discover that the general public, and our backers along with them, were more interested in 140 characters, fitness trackers, and Grumpy Cat videos. The greatest, most powerful deep-learning computers in humanity’s existence were inside Google and Facebook—and all humanity had to show for it were adwords, sponsored links, and teenagers hooked on sending one another pictures of their genitals.

The Scheffe test is the most conservative, the Tukey test is best when many comparisons are made (when there are many groups), and the Bonferroni test is preferred when few comparisons are made. However, these post-hoc tests often support the same conclusions.3 To illustrate, let’s say the independent variable has three categories. Then, a post-hoc test will examine hypotheses for whether . In addition, these tests will also examine which categories have means that are not significantly different from each other, hence, providing homogeneous subsets. An example of this approach is given later in this chapter. Knowing such subsets can be useful when the independent variable has many categories (for example, classes of employees). Figure 13.1 ANOVA: Significant and Insignificant Differences Eta-squared (η2) is a measure of association for mixed nominal-interval variables and is appropriate for ANOVA. Its values range from zero to one, and it is interpreted as the percentage of variation explained. It is a directional measure, and computer programs produce two statistics, alternating specification of the dependent variable. Finally, ANOVA can be used for testing interval-ordinal relationships. We can ask whether the change in means follows a linear pattern that is either increasing or decreasing. For example, assume we want to know whether incomes increase according to the political orientation of respondents, when measured on a seven-point Likert scale that ranges from very liberal to very conservative. If a linear pattern of increase exists, then a linear relationship is said to exist between these variables. Most statistical software packages can test for a variety of progressive relationships. ANOVA Assumptions ANOVA assumptions are essentially the same as those of the t-test: (1) the dependent variable is continuous, and the independent variable is ordinal or nominal, (2) the groups have equal variances, (3) observations are independent, and (4) the variable is normally distributed in each of the groups. The assumptions are tested in a similar manner. Relative to the t-test, ANOVA requires a little more concern regarding the assumptions of normality and homogeneity. First, like the t-test, ANOVA is not robust for the presence of outliers, and analysts examine the presence of outliers for each group. Also, ANOVA appears to be less robust than the t-test for deviations from normality. Second, regarding groups having equal variances, our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups; the test of homogeneity is a strict test, testing for any departure from equal variances, and in practice, groups may have neither equal variances nor substantial differences in the amount of variances. In these instances, a visual finding of no substantial differences suffices. Other strategies for dealing with heterogeneity are variable transformations and the removal of outliers, which increase variance, especially in small groups. Such outliers are detected by examining boxplots for each group separately. Also, some statistical software packages (such as SPSS), now offer post-hoc tests when equal variances are not assumed.4 A Working Example The U.S. Environmental Protection Agency (EPA) measured the percentage of wetland loss in watersheds between 1982 and 1992, the most recent period for which data are available (government statistics are sometimes a little old).5 An analyst wants to know whether watersheds with large surrounding populations have

Traditional 401(K) or 403(B) Account Typically offered by your employer, a 401(k) account allows you to invest a percentage of your wages for retirement. A 403(b) is the public sector’s equivalent to a 401(k). Investing through a 401(k) or 403(b) is one of the most advantageous ways to invest, since the government is giving you tax breaks. Your employer will sometimes match what you contribute, up to a certain percent. (FreE mONaY!) Remember from our Financial Game Plan that this is the trump card: if you have an employer match, take advantage of it. Maximum yearly contribution: $20,500, which means you can contribute any amount up to that limit. This does not include any employer match, so go crazy. (This and all other retirement account maximums are current for the 2022 tax year.) Individual Retirement Account (IRA) This is an individual retirement account, meaning it’s not tied to your employer. You have to open it up on your own, and it’s yours forever. Good news: you can have both a 401(k) and an IRA! Maximum yearly contribution: $6,000. You technically have fifteen and a half months to contribute that $6,000. The government lets you put money in your IRA during the twelve months of that year, plus the first months of the following year leading up to the tax filing deadline. A little confusing, but stay with me: if you want to contribute to your IRA in 2023, you will have from January to December 2023, plus January to April 15, 2024, to hit that $6,000 max. So, let’s say that you’re rounding out the year of contributions at $4,500. That means you have another three-ish months to get the full $6,000! More time, yay! If we’re already in the new year, and you want the money to specifically go to the previous year’s IRA, you simply need to specify that when you contribute. It’s usually as easy as checking a “previous year” box. Let’s talk about the most common retirement accounts. In addition to the differences above, 401(k) and IRA accounts come in two flavors: traditional and Roth. The main difference between these accounts is in how they’re taxed. In traditional accounts, you won’t pay any taxes on this money until you withdraw it at retirement. You get the tax benefits now. Roth accounts require tax payments now, so you don’t have to pay them later. You get the tax benefits later. In some cases, you can make both traditional and Roth contributions into the same account.

NIM and spread are the two key parameters that give an indication of a bank's operational efficiency. As a concept, NIM and spread are similar, but there is a subtle difference between the two. While NIM is arrived at by dividing a bank's net interest income by its average interest-earning assets, spread is the margin between the yield on assets and the cost of liabilities, or the difference between interest income and interest expense as a percentage of assets. NIM can be higher or lower than the net interest spread.

At the heart of the decoding problem is how to understand the vast information contained in neural signals, the challenge of what is being called "big data". For neuroscientists, big data is a means for exploring populations of neurons to discover the macroscopic signatures of dynamical systems, rather than attempting to make sense of the activity of individual neurons. Two surprising results from numerous experiments recording from neurons in different brain regions have revealed a wonderful secret of nature about the relation between the number of neurons recorded and and their dimensionality (the number of principal components required to explain a fixed percentage of variance). First, the dimensionality of the neural data is much smaller than the number of recorded neurons. Second, when dimensionality procedures are used to extract neuronal state dynamics, the resulting low-dimensional neural trajectories reveal portraits of the behavior of a dynamical system. This means that it may not be necessary to record from many more neurons within a brain region in order to accurately recover its internal state-space dynamics.

Mortgages were short-term, usually for three to five years, and they were not amortized. In other words, people paid interest, but did not repay the sum they had borrowed (the principal) until the end of the loan’s term, so that they ended up facing a balloon-sized final payment. The average difference (spread) between mortgage rates and high-grade corporate bond yields was about two percentage points during the 1920s, compared with about half a per cent (50 basis points) in the past twenty years.

A few years ago, Kobe [Bryant, duh] fractured the fourth metacarpal bone in his right hand. He missed the first fifteen games of the season; he used the opportunity to learn to shoot jump shots with his left, which he has been known to do in games. While it was healing, the ring finger, the one adjacent to the break, spend a lot of time taped to his pinkie. In the end, Kobe discovered, his four fingers were no longer evenly spaced; now they were separated, two and two. As a result, his touch on the ball was different, his shooting percentage went down. Studying the film he noticed that his shots were rotating slightly to the right. To correct the flaw, Kobe went to the gym over the summer and made one hundred thousand shots. that's one hundred thousand made, not taken. He doesn't practice taking shots, he explains. He practices making them. If you're clear on the difference between the two ideas, you can start drawing a bead on Kobe Bryant who may well be one of the most misunderstood figures in sports today. Scito Hoc Super Omnia by Mike Sager for Esquire Magazine Nov 2007

In their urge for survival, the seed-bearing trees hit upon countless different devices for carrying pollen from one flower to another, but essentially the methods fall into two main categories. The first is wind-pollination, which requires the presence of light, small, dry pollen grains, easily shaken from the stamens, or male flowers. To receive the tiny bits of pollen that are blown about by the wind, the stigmata of flowers must be long, or feathery, or sticky, or so constructed as to trap the fine dust. All conifers are pollinated this way, as are the poplar, ash, birch, oak, beech, and certain other species. But since this method is so haphazard, a disproportionately high percentage of pollen is wasted and these trees must produce immense quantities of pollen in order that even a tiny amount will be effective. Scientists have estimated that a single stamen of a beech tree, for example, may yield 2,000 grains, while the branch system of a vigorous young birch can produce 100 million grains a year. One pine or spruce cone alone releases between 1 and 2 million grains of pollen into the air: In Sweden, which is covered with spruce forests, an estimated 75,000 tons of pollen are blown from the trees each year.

only a small minority of military personnel have combat-related jobs. In 2015, even after two lengthy wars, the percentage of military personnel in combat specialties was only 14 percent overall—with substantial differences between the services: for instance, 28 percent of enlisted Army personnel serve in jobs that are classified as combat positions compared to just 3 percent of Navy enlisted personnel. To be sure, many military personnel in noncombat positions end up in combat [zones] anyway. . . . But even when deployed in combat zones, most members of the military never end up fighting.

NBA players made roughly the same percentage of shots from 23 feet as they did from 24. But because the three-point line ran between them, the values of those two shots were radically different. Shot attempts from 23 feet had an average value of 0.76 points, while 24-footers were worth 1.09. This, the Warriors concluded, was an opportunity. By moving back a few inches before shooting, a basketball player could improve his rate of return by 43 percent.41