29 September 2006

Robert Heller - Collected Quotes

"A project is only as sound as its weakest assumption, or its largest uncertainty." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"As in war, strategic success depends on tactical effectiveness, and no degree of planning can lessen management's tactical imperatives. The first responsibility of the executive, anyway, is to the here and now. If he makes a shambles of the present, there may be no future; and the real purpose of planning - the one whose neglect is common, but poisonous - is to safeguard and sustain the company in subsequent short-run periods." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"Faced with a decision, always ask one implacable question: If this project fails, if the worst comes to the worst, what will be the result? If the answer is total corporate disaster, drop the project. If the worst possible outcome is tolerable, say, break-even, the executive has the foundation of all sound decision making - a fail-safe position." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"What goes wrong [in long-range planning] is that sensible anticipation gets converted into foolish numbers: and their validity always hinges on large loose assumptions." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"A good sign that either the meeting or some of the people are superfluous is when they try to get out of coming." (Robert Heller, "The Supermanagers", 1984)

"Effective management always means asking the right question." (Robert Heller, "The Supermanagers", 1984)

"No talent in management is worth more than the ability to master facts - not just any facts, but the ones that provide the best answers. Mastery thus involves knowing what facts you want; where to dig for them; how to dig; how to process the mined ore; and how to use the precious nuggets of information that are finally in your hand. The process can be laborious - which is why it is so often botched." (Robert Heller, "The Supermanagers", 1984)

"Decisions should be pushed down as far as possible, to the level of competence. This allows senior managers more time for making decisions of a more complex nature."(Robert Heller, "The Pocket Manager", 1987)

"[decision trees are the] most picturesque of all the allegedly scientific aids to making decisions. The analyst charts all the possible outcomes of different options, and charts all the latters' outcomes, too. This produces a series of stems and branches (hence the tree). Each of the chains of events is given a probability and a monetary value." (Robert Heller, "The Pocket Manager", 1987)

"Management: The definition that includes all the other definitions in this book and which, because of that, is the most general and least precise. Its concrete, people meaning - the board of directors and all executives with the power to make decisions - is no problem, except for the not-so-little matter of where to draw the line between managers who are part of 'the management' and managers who are not. (Robert Heller, "The Pocket Manager", 1987)

"No decision in business provides greater potential for the creation of wealth (or its destruction, come to think of it) than the choice of which innovation to back." (Robert Heller, "The Decision Makers", 1989)

Randall E Schumacker - Collected Quotes

"Given the important role that correlation plays in structural equation modeling, we need to understand the factors that affect establishing relationships among multivariable data points. The key factors are the level of measurement, restriction of range in data values (variability, skewness, kurtosis), missing data, nonlinearity, outliers, correction for attenuation, and issues related to sampling variation, confidence intervals, effect size, significance, sample size, and power." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"Need to consider outliers as they can affect statistics such as means, standard deviations, and correlations. They can either be explained, deleted, or accommodated (using either robust statistics or obtaining additional data to fill-in). Can be detected by methods such as box plots, scatterplots, histograms or frequency distributions." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"Outliers or influential data points can be defined as data values that are extreme or atypical on either the independent (X variables) or dependent (Y variables) variables or both. Outliers can occur as a result of observation errors, data entry errors, instrument errors based on layout or instructions, or actual extreme values from self-report data. Because outliers affect the mean, the standard deviation, and correlation coefficient values, they must be explained, deleted, or accommodated by using robust statistics." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"Structural equation modeling is a correlation research method; therefore, the measurement scale, restriction of range in the data values, missing data, outliers, nonlinearity, and nonnormality of data affect the variance–covariance among variables and thus can impact the SEM analysis." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"Structural equation modeling (SEM) uses various types of models to depict relationships among observed variables, with the same basic goal of providing a quantitative test of a theoretical model hypothesized by the researcher. More specifically, various theoretical models can be tested in SEM that hypothesize how sets of variables define constructs and how these constructs are related to each other." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

"There are several key issues in the field of statistics that impact our analyses once data have been imported into a software program. These data issues are commonly referred to as the measurement scale of variables, restriction in the range of data, missing data values, outliers, linearity, and nonnormality." (Randall E Schumacker & Richard G Lomax, "A Beginner’s Guide to Structural Equation Modeling" 3rd Ed., 2010)

26 September 2006

Donella H Meadows - Collected Quotes

"Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head - my mental models. None of these is or ever will be the real world. […] Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us." (Donella H Meadows, "Limits to Growth", 1972)

"However, and conversely, our models fall far short of representing the world fully. That is why we make mistakes and why we are regularly surprised. In our heads, we can keep track of only a few variables at one time. We often draw illogical conclusions from accurate assumptions, or logical conclusions from inaccurate assumptions. Most of us, for instance, are surprised by the amount of growth an exponential process can generate. Few of us can intuit how to damp oscillations in a complex system." (Donella H Meadows, "Limits to Growth", 1972)

"Technology can relieve the symptoms of a problem without affecting the underlying causes. Faith in technology as the ultimate solution to all problems can thus divert our attention from the most fundamental problem - the problem of growth in a finite system - and prevent us from taking effective action to solve it." (Donella H Meadows, "The Limits to Growth", 1972)

"Models can easily become so complex that they are impenetrable, unexaminable, and virtually unalterable." (Donella H Meadows, "The unavoidable a priori", 1980)

"The world is a complex, interconnected, finite, ecological–social–psychological–economic system. We treat it as if it were not, as if it were divisible, separable, simple, and infinite. Our persistent, intractable global problems arise directly from this mismatch." (Donella H Meadows, "Whole Earth Models and System", 1982)

"A quantity growing exponentially toward a limit reaches that limit in a surprisingly short time." (Donella Meadows, "Thinking in systems: A Primer", 2008)

"A system is a set of things – people, cells, molecules, or whatever – interconnected in such a way that they produce their own pattern of behavior over time. […] The system, to a large extent, causes its own behavior." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008) 

"In fact, one of the most frustrating aspects of systems is that the purposes of subunits may add up to an overall behavior that no one wants." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)"

"In physical, exponentially growing systems, there must be at least one reinforcing loop driving growth and at least one balancing feedback loop constraining growth, because no system can grow forever in a finite environment." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"Like resilience, self-organizazion is often sacrificed for purposes of short-term productivity and stability." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"Our culture, obsessed with numbers, has given us the idea that what we can measure is more important than what we can't measure. Think about that for a minute. It means that we make quantity more important than quality." (Donella H Meadows, "Thinking in Systems: A Primer", 2008)

"[…] our mental models fail to take into account the complications of the real world - at least those ways that one can see from a systems perspective. It is a warning list. Here is where hidden snags lie. You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long-term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays. You are likely to mistreat, misdesign, or misread systems if you don’t respect their properties of resilience, self-organization, and hierarchy." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"The bounded rationality of each actor in a system may not lead to decisions that further the welfare of the system as a whole." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"The world is nonlinear. Trying to make it linear for our mathematical or administrative convenience is not usually a good idea even when feasible, and it is rarely feasible." (Donella H Meadow, "Thinking in Systems: A Primer", 2008)

"When there are long delays in feedback loops, some sort of foresight is essential." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays." (Donella H Meadow, "Thinking in Systems: A Primer", 2008)

25 September 2006

Peter Coad - Collected Quotes

"More effective analysis requires the use of problem domain constructs, both for present reuse and for future reuse." (Peter Coad & Edward Yourdon, "Object-Oriented Analysis" 2nd Ed., 1991)

"One of the biggest problems faced by analysts is studying the problem domain and making discoveries about it. [...] OOA is the challenge of understanding the problem domain, and then the system's responsibilities in that light." (Peter Coad & Edward Yourdon, "Object-Oriented Analysis" 2nd Ed., 1991)

"One of the critical success factors for any method and its application is its ability to facilitate communication, avoiding information  overload. So for larger models, the question is how to guide the reader into different parts of the model." (Peter Coad & Edward Yourdon, "Object-Oriented Analysis" 2nd Ed., 1991)

"The transition from analysis to design has been a constant source of frustration. [...] no matter how many cute cartoons are drawn to depict the transition, the radical change in underlying representation causes a major chasm between analysis and design models." (Peter Coad & Edward Yourdon, "Object-Oriented Analysis" 2nd Ed., 1991)

"A pattern is a fully realized form original, or model accepted or proposed for imitation. With patterns, small piecework is standardized into a larger chunk or unit. Patterns become the building blocks for design and construction. Finding and applying patterns indicates progress in a field of human endeavor." (Peter Coad, "Object-oriented patterns", 1992)

"Object-oriented methods tend to focus on the lowest-level building block: the class and its objects." (Peter Coad, "Object-oriented patterns", 1992)

"With each pattern, small piecework is standardized into a larger chunk or unit. Patterns become the building blocks for design and construction. Finding and applying patterns indicates progress in a field of human endeavor." (Peter Coad, "Object-oriented patterns", 1992)

"We think most process initiatives are silly. Well-intentioned managers and teams get so wrapped up in executing processes that they forget that they are being paid for results, not process execution. (Peter Coad et al, "Java Modeling in Color with UML", 1999)

Larry A Wasserman - Collected Quotes

 "A smaller model with fewer covariates has two advantages: it might give better predictions than a big model and it is more parsimonious (simpler). Generally, as you add more variables to a regression, the bias of the predictions decreases and the variance increases. Too few covariates yields high bias; this called underfitting. Too many covariates yields high variance; this called overfitting. Good predictions result from achieving a good balance between bias and variance. […] fiding a good model involves trading of fit and complexity." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Bayesian inference is a controversial approach because it inherently embraces a subjective notion of probability. In general, Bayesian methods provide no guarantees on long run performance." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Bayesian inference is appealing when prior information is available since Bayes’ theorem is a natural way to combine prior information with data. Some people find Bayesian inference psychologically appealing because it allows us to make probability statements about parameters. […] In parametric models, with large samples, Bayesian and frequentist methods give approximately the same inferences. In general, they need not agree." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Inequalities are useful for bounding quantities that might otherwise be hard to compute." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Probability is a mathematical language for quantifying uncertainty." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Statistical inference, or 'learning' as it is called in computer science, is the process of using data to infer the distribution that generated the data." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"[…] studying methods for parametric models is useful for two reasons. First, there are some cases where background knowledge suggests that a parametric model provides a reasonable approximation. […] Second, the inferential concepts for parametric models provide background for understanding certain nonparametric methods." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The Bayesian approach is based on the following postulates: (B1) Probability describes degree of belief, not limiting frequency. As such, we can make probability statements about lots of things, not just data which are subject to random variation. […] (B2) We can make probability statements about parameters, even though they are fixed constants. (B3) We make inferences about a parameter θ by producing a probability distribution for θ. Inferences, such as point estimates and interval estimates, may then be extracted from this distribution." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The frequentist point of view is based on the following postulates: (F1) Probability refers to limiting relative frequencies. Probabilities are objective properties of the real world. (F2) Parameters are i xed, unknown constants. Because they are not fluctuating, no useful probability statements can be made about parameters. (F3) Statistical procedures should be designed to have well-defined long run frequency properties." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"The important thing is to understand that frequentist and Bayesian methods are answering different questions. To combine prior beliefs with data in a principled way, use Bayesian inference. To construct procedures with guaranteed long run performance, such as confidence intervals, use frequentist methods. Generally, Bayesian methods run into problems when the parameter space is high dimensional." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004) 

"The most important aspect of probability theory concerns the behavior of sequences of random variables." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"There is a tendency to use hypothesis testing methods even when they are not appropriate. Often, estimation and confidence intervals are better tools. Use hypothesis testing only when you want to test a well-defined hypothesis." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algorithms are more scalable than statisticians ever thought possible. Formal statistical theory is more pervasive than computer scientists had realized." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Undirected graphs are an alternative to directed graphs for representing independence relations. Since both directed and undirected graphs are used in practice, it is a good idea to be facile with both. The main difference between the two is that the rules for reading independence relations from the graph are different." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

24 September 2006

Tom DeMarco - Collected Quotes

"As a general rule of them, when benefits are not quantified at all, assume there aren’t any.” (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Human interactions are complicated and never very crisp and clean in their effects, but they matter more than any other aspect of the work." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Managers jeopardize product quality by setting unreachable deadlines. They don’​​​​​​t think about their action in such terms; they think rather that what they’​​​​​​re doing is throwing down an interesting challenge to their workers, something to help them strive for excellence." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Most of us managers are prone to one failing: A tendency to manage people as though they were modular components."  (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"People under time pressure don’​​​​​​t work better - ​​​​​​they just work faster. In order to work faster, they may have to sacrifice the quality of the product and of their own work experience." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Programmers seem to be a bit more productive after they’​​​​​​ve done the estimate themselves, compared to cases in which the manager did it without even consulting them." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"[The common definition of estimate is] 'the most optimistic prediction that has a non-zero probability of coming true' [...] Accepting this definition leads irrevocably toward a method called what's-the-earliest-date-by-which-you-can't-prove-you-won't-be-finished estimating" (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"The major problems of our work are not so much technological as sociological in nature. Most managers are willing to concede the idea that they’​​​​​​ve got more people worries than technical worries. But they seldom manage that way. They manage as though technology were their principal concern. They spend their time puzzling over the most convoluted and most interesting puzzles that their people will have to solve, almost as though they themselves were going to do the work rather than manage it. […] The main reason we tend to focus on the technical rather than the human side of the work is not because it’​​​​​​s more crucial, but because it’​​​​​​s easier to do." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

 "The obsession with methodologies in the workplace is another instance of the high-tech illusion. It stems from the belief that what really matters is the technology. [...] Whatever the technological advantage may be, it may come only at the price of a significant worsening of the team's sociology." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"The [software] builders’​​​​​​ view of quality, on the other hand, is very different. Since their self-esteem is strongly tied to the quality of the product, they tend to impose quality standards of their own. The minimum that will satisfy them is more or less the best quality they have achieved in the past. This is invariably a higher standard than what the market requires and is willing to pay for." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Change always implies abandonment. What you're abandoning is an old way of doing things. You're abandoning it because it's old, because time has made it no longer the best way. But it is also (again because it's old) a familiar way. And more important, it is an approach that people have mastered. So the change you are urging upon your people requires them to abandon their mastery of the familiar, and to become novices once again, to become rank beginners at something with self-definitional importance." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"The premise here is that the hierarchy lines on the chart are also the only communication conduit. Information can flow only along the lines. [...] The hierarchy lines are paths of authority. When communication happens only over the hierarchy lines, that's a priori evidence that the managers are trying to hold on to all control. This is not only inefficient but an insult to the people underneath." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"The business of believing only what you have a right to believe is called risk management." (Tom DeMarco & Timothy Lister, "Waltzing with Bears: Managing Risk on Software Projects", 2003)

23 September 2006

Alberto Cairo - Collected Quotes

"All graphics present data and allow a certain degree of exploration of those same data. Some graphics are almost all presentation, so they allow just a limited amount of exploration; hence we can say they are more infographics than visualization, whereas others are mostly about letting readers play with what is being shown, tilting more to the visualization side of our linear scale. But every infographic and every visualization has a presentation and an exploration component: they present, but they also facilitate the analysis of what they show, to different degrees." (Alberto Cairo, "The Functional Art", 2011)

"But if you don’t present your data to readers so they can see it, read it, explore it, and analyze it, why would they trust you?" (Alberto Cairo, "The Functional Art", 2011)

"By giving numbers a proper shape, by visually encoding them, the graphic has saved you time and energy that you would otherwise waste if you had to use a table that was not designed to aid your mind." (Alberto Cairo, "The Functional Art", 2011)

"For too many traditional journalists, infographics are mere ornaments to make the page look lighter and more attractive for audiences who grow more impatient with long-form stories every day. Infographics are treated not as devices that expand the scope of our perception and cognition, but as decoration." (Alberto Cairo, "The Functional Art", 2011)

"[...] graphical displays can be either figurative or non-figurative.[…] Other graphics that display abstract phenomena are non-figurative. In these ,there is no mimetic correspondence between what is being represented and its representation. The relationship between those two entities is conventional, no tnatural [...]." (Alberto Cairo, "The Functional Art", 2011)

"Graphics, charts, and maps aren’t just tools to be seen, but to be read and scrutinized. The first goal of an infographic is not to be beautiful just for the sake of eye appeal, but, above all, to be understandable first, and beautiful after that; or to be beautiful thanks to its exquisite functionality." (Alberto Cairo, "The Functional Art", 2011)

"if you want to show change through time, use a time-series chart; if you need to compare, use a bar chart; or to display correlation, use a scatter-plot - because some of these rules make good common sense." (Alberto Cairo, "The Functional Art", 2011) 

"In information graphics, what you show can be as important as what you hide." (Alberto Cairo, "The Functional Art", 2011)

"Information consumption can lead to higher knowledge on the part of the audience, if its members are able to perceive the patterns or meaning of data. It is not a passive process; our brains are not hard drives that store stuff uncritically .When people see, read, or listen, they assimilate content by relating it to their memories and experiences." (Alberto Cairo, "The Functional Art", 2011)

"It is not possible to be a good communicator if you have not developed a keen interest in almost everything as well as an urge to learn as much as you can about the strangest, most varied, unrelated topics. The life of a visual communicator should be one of systematic and exciting intellectual chaos." (Alberto Cairo, "The Functional Art", 2011)

" [...] the better defined the goals of an artifact, the narrower the variety of forms it can adopt." (Alberto Cairo, "The Functional Art", 2011)

"The fact that an information graphic is designed to help us complete certain intellectual tasks is what distinguishes it from fine art." (Alberto Cairo, "The Functional Art", 2011)

"The first and main goal of any graphic and visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach." (Alberto Cairo, "The Functional Art", 2011)

"[...] the form of a technological object must depend on the tasks it should help with. This is one of the most important principles to remember when dealing with infographics and visualizations: The form should be constrained by the functions of your presentation. There may be more than one form a data set can adopt so that readers can perform operations with it and extract meanings, but the data cannot adopt any form. Choosing visual shapes to encode information should not be based on aesthetics and personal tastes alone." (Alberto Cairo, "The Functional Art", 2011)

"[...] the human brain is not good at calculating surface sizes. It is much better at comparing a single dimension such as length or height. [...] the brain is also a hopelessly lazy machine." (Alberto Cairo, "The Functional Art", 2011)

"[...] the relationship between forms and functions is bidirectional. Form doesn’t always follow function; in many cases, the function follows a form that previously followed another unrelated function." (Alberto Cairo, "The Functional Art", 2011)

"The overuse of bubble charts in news media is a good example of how infographics departments can become more worried about how their projects look than with how they work." (Alberto Cairo, "The Functional Art", 2011)

"Thinking of graphics as art leads many to put bells and whistles over substance and to confound infographics with mere illustrations." (Alberto Cairo, "The Functional Art", 2011)

"We reach wisdom when we achieve a deep understanding of acquired knowledge, when we not only 'get it', but when new information blends with prior experience so completely that it makes us better at knowing what to do in other situations, even if they are only loosely related to the information from which our original knowledge came. Just as not all the information we absorb leads to knowledge, not all of the knowledge we acquire leads to wisdom." (Alberto Cairo, "The Functional Art", 2011)

"What is really important is to remember that no matter how creative and innovative you wish to be in your graphics and visualizations, the first thing you must do, before you put a finger on the computer keyboard, is ask yourself what users are likely to try to do with your tool." (Alberto Cairo, "The Functional Art", 2011)

"[...] without conscious effort, the brain always tries to close the distance between observed phenomena and knowledge or wisdom that can help us survive. This is what cognition means. The role of an information architect is to anticipate this process and generate order before people’s brains try to do it on their own." (Alberto Cairo, "The Functional Art", 2011)

"An infographic is an edited, summarized presentation of data selected by a designer to tell a story. A visualization is a display designed to explore data so every reader will be able to extract his or her own stories" (Alberto Cairo)

Edward Yourdon - Collected Quotes

"There is nothing in the programming field more despicable than an undocumented program." (Edward Yourdon, "Techniques of program structure and design", 1975)

"One of the critical success factors for any method and its application is its ability to facilitate communication, avoiding information  overload. So for larger models, the question is how to guide the reader into different parts of the model."

"One of the critical success factors for any method and its application is its ability to facilitate communication, avoiding information  overload. So for larger models, the question is how to guide the reader into different parts of the model." (Peter Coad & Edward Yourdon, "Object-Oriented Analysis" 2nd Ed., 1991)

"[Object-oriented analysis is] the challenge of understanding the problem domain and then the system's responsibilities in that light." (Edward Yourdon, "Object-Oriented Design", 1991) 

"To us, analysis is the study of a problem domain, leading to a specification of externally observable behavior; a complete, consistent, and feasible statement of what is needed; a coverage of both functional and quantified operational characteristics (e. g. reliability, availability, performance)." (Edward Yourdon, Object-oriented design, 1991)

"All projects are behind schedule - it's just a question of when you discover that fact and how the project team will respond to the discovery. So you might as well plan for it: Create an extreme artificial crisis in the early days of the project and observe what happens." (Edward Yourdon, "Death March", 1997)

"System dynamics is a method for studying the world around us. Unlike other scientists, who study the world by breaking it up into smaller and smaller pieces, system dynamicists look at things as a whole. The central concept to system dynamics is understanding how all the objects in a system interact with one another. A system can be anything from a steam engine, to a bank account, to a basketball team. The objects and people in a system interact through 'feedback' loops, where a change in one variable affects other variables over time, which in turn affects the original variable, and so on." (Edward Yourdon, "Death March", 1997)

22 September 2006

Robert M Pirsig - Collected Quotes

"An experiment is a failure only when it also fails adequately to test the hypothesis in question, when the data it produces don't prove anything one way or the other." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"Laws of nature are human inventions, like ghosts. Laws of logic, or mathematics are also human inventions, like ghosts. The whole blessed thing is a human invention, including the idea that it isn't a human invention." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"Quality is a direct experience independent of and prior to intellectual abstractions." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"Technology presumes there's just one right way to do things and there never is." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"The number of rational hypotheses that can explain any given phenomenon is infinite." (Robert M. Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"The solutions all are simple - after you have arrived at them. But they're simple only when you know already what they are."(Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"The world comes to us in an endless stream of puzzle pieces that we would like to think all fit together somehow, but that in fact never do."(Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"Traditional scientific method has always been at the very best 20-20 hindsight. It's good for seeing where you've been. It's good for testing the truth of what you think you know, but it can't tell you where you ought to go." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"When analytic thought, the knife, is applied to experience, something is always killed in the process." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"Within a Metaphysics of Quality, science is a set of static intellectual patterns describing this reality, but the patterns are not the reality they describe." (Robert M Pirsig, "Zen and the Art of Motorcycle Maintenance", 1974)

"Data without generalization is just gossip." (Robert M Pirsig, "Lila: An Inquiry into Morals", 1991)

19 September 2006

Joseph Weizenbaum - Collected Quotes

"A higher-level formal language is an abstract machine." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation", 1976)

"A theory is, of course, not merely any grammatically correct text that uses a set of terms somehow symbolically related to reality. It is a systematic aggregate of statements of laws. Its content, its very value as theory, lies at least as much in the structure of the interconnections that relate its laws to one another, as in the laws themselves." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Computers make possible an entirely new relationship between theories and models. I have already said that theories are texts. Texts are written in a language. Computer languages are languages too, and theories may be written in them. Indeed, for the present purpose we need not restrict our attention to machine languages or even to the kinds of 'higher-level' languages we have discussed. We may include all languages, specifically also natural languages, that computers may be able to interpret. The point is precisely that computers do interpret texts given to them, in other words, that texts determine computers' behavior. Theories written in the form of computer programs are ordinary theories as seen from one point of view." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Machines, when they operate properly, are not merely law abiding; they are embodiments of law. To say that a specific machine is 'operating properly' is to assert that it is an embodiment of a law we know and wish to apply. We expect an ordinary desk calculator, for example, to be an embodiment of the laws of arithmetic we all know." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"Man is not a machine, [...] although man most certainly processes information, he does not necessarily process it in the way computers do. Computers and men are not species of the same genus. [...] No other organism, and certainly no computer, can be made to confront genuine human problems in human terms. [...] However much intelligence computers may attain, now or in the future, theirs must always be an intelligence alien to genuine human problems and concerns." (Joesph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation, 1976)

"Programming systems can, of course, be built without plan and without knowledge, let alone understanding, of the deep structural issues involved, just as houses, cities, systems of dams, and national economic policies can be similarly hacked together. As a system so constructed begins to get large, however, it also becomes increasingly unstable. When one of its subfunctions fails in an unanticipated way, it may be patched until the manifest trouble disappears. But since there is no general theory of the whole system, the system itself can be only a more or less chaotic aggregate of subsystems whose influence on one another's behavior is discoverable only piecemeal and by experiment. The hacker spends part of his time at the console piling new subsystems onto the structure he has already built - he calls them 'new features' - and the rest of his time in attempts to account for the way in which substructures already in place misbehave. That is what he and the computer converse about." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The aim of the model is of course not to reproduce reality in all its complexity. It is rather to capture in a vivid, often formal, way what is essential to understanding some aspect of its structure or behavior." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The computer programmer is a creator of universes for which he alone is the lawgiver. No playwright, no stage director, no emperor, however powerful, has ever exercised such absolute authority to arrange a stage or field of battle and to command such unswervingly dutiful actors or troops." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"The connection between a model and a theory is that a model satisfies a theory; that is, a model obeys those laws of behavior that a corresponding theory explicitly states or which may be derived from it. [...] Computers make possible an entirely new relationship between theories and models. [...] A theory written in the form of a computer program is [...] both a theory and, when placed on a computer and run, a model to which the theory applies." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

"There is a distinction between physically embodied machines, whose ultimate function is to transduce energy or deliver power, and abstract machines. i.e., machines that exist only as ideas. The laws which the former embody must be a subset of the laws that govern the real world. The laws that govern the behavior of abstract machines are not necessarily so constrained. One may, for example, design an abstract machine whose internal signals are propagated among its components at speeds greater than the speed of light, in clear violation of physical law. The fact that such a machine cannot actually be built does not prohibit the exploration of its behavior." (Joseph Weizenbaum, "Computer power and human reason: From judgment to calculation" , 1976)

17 September 2006

Fernando J Corbató - Collected Quotes

"Systems with unknown behavioral properties require the implementation of iterations which are intrinsic to the design process but which are normally hidden from view. Certainly when a solution to a well-understood problem is synthesized, weak designs are mentally rejected by a competent designer in a matter of moments. On larger or more complicated efforts, alternative designs must be explicitly and iteratively implemented. The designers perhaps out of vanity, often are at pains to hide the many versions which were abandoned and if absolute failure occurs, of course one hears nothing. Thus the topic of design iteration is rarely discussed. Perhaps we should not be surprised to see this phenomenon with software, for it is a rare author indeed who publicizes the amount of editing or the number of drafts he took to produce a manuscript." (Fernando J Corbató, "A Managerial View of the Multics System Development", 1977)

"Because one has to be an optimist to begin an ambitious project, it is not surprising that underestimation of completion time is the norm." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"Design bugs are often subtle and occur by evolution with early assumptions being forgotten as new features or uses are added to systems." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"It is important to emphasize the value of simplicity and elegance, for complexity has a way of compounding difficulties and as we have seen, creating mistakes. My definition of elegance is the achievement of a given functionality with a minimum of mechanism and a maximum of clarity." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"The value of metaphors should not be underestimated. Metaphors have the virtue of an expected behavior that is understood by all. Unnecessary communication and misunderstandings are reduced. Learning and education are quicker. In effect metaphors are a way of internalizing and abstracting concepts allowing one's thinking to be on a higher plane and low-level mistakes to be avoided." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

16 September 2006

Karl E Weick - Collected Quotes

"If all of the elements in a large system are loosely coupled to one another, then any one element can adjust to and modify a local a local unique contingency without affecting the whole system. These local adaptations can be swift, relatively economical, and substantial." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"In a loosely coupled system there is more room available for self-determination by the actors. If it is argued that a sense of efficacy is crucial for human beings. when a sense of efficacy might be greater in a loosely coupled system with autonomous units than it would be in a tightly coupled system where discretion is limited." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"Managers construct, rearrange, single out, and demolish many objective features of their surroundings. When people act they unrandomize variables, insert vestiges of orderliness, and literally create their own constraints." (Karl E Weick, "Social Psychology of Organizing", 1979)

"The typical coupling mechanisms of authority of office and logic of the task do not operate in educational organizations." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"Any approach to the study of organizations is built on specific assumptions about the nature of organizations and how they are designed and function." (Richard L Daft & Karl E Weick, "Toward a model of organizations as interpretation systems", Academy of Management Review Vol 9 (2), 1984)

"Action often creates the orderly relations that originally were mere presumptions summarized in a cause map. Thus language trappings of organizations such as strategic plans are important components in the process of creating order. They hold events together long enough and tightly enough in people's heads so that they act in the belief that their actions will be influential and make sense." (Karl E. Weick, "Organizational culture as a source of high reliability", 1987)

"An ordered set of assertions about a generic behavior or structure assumed to hold throughout a significantly broad range of specific instances." (Karl E Weick, "Theory construction as disciplined imagination", 1989)

"Experience is the consequence of activity. The manager literally wades into the swarm of 'events' that surround him and actively tries to unrandomize them and impose some order: The manager acts physically in the environment, attends to some of it, ignores most of it, talks to other people about what they see and are doing."  (Karl E Weick, "Sensemaking in Organizations", 1995)

"Organizations are presumed to talk to themselves over and over to find out what they are thinking." (Karl E Weick, "Sensemaking in Organizations", 1995)

"Sensemaking is about the enlargement of small cues. It is a search for contexts within which small details fit together and make sense. It is people interacting to flesh out hunches. It is a continuous alternation between particulars and explanations with each cycle giving added form and substance to the other." (Karl E Weick, "Sensemaking in Organizations", 1995)

"Sensemaking tends to be swift, which means we are more likely to see products than processes." (Karl E Weick, Sensemaking in Organizations, 1995)

"The organism or group enacts equivocal raw talk, the talk is viewed retrospectively, sense is made of it, and then this sense is stored as knowledge in the retention process. The aim of each process has been to reduce equivocality and to get some idea of what has occurred." (Karl E Weick, "Sensemaking in Organizations", 1995)

"The point we want to make here is that sensemaking is about plausibility, coherence, and reasonableness. Sensemaking is about accounts that are socially acceptable and credible... It would be nice if these accounts were also accurate. But in an equivocal, postmodern world, infused with the politics of interpretation and conflicting interests and inhabited by people with multiple shifting identities, an obsession with accuracy seems fruitless, and not of much practical help, either." (Karl E Weick, "Sensemaking in Organizations", 1995)

"To talk about sensemaking is to talk about reality as an ongoing accomplishment that takes form when people make retrospective sense of the situations in which they find themselves and their creations. There is a strong reflexive quality to this process. People make sense of things by seeing a world on which they already imposed what they believe. In other words, people discover their own inventions. This is why sensemaking can be understood as invention and interpretations understood as discovery. These are complementary ideas. If sensemaking is viewed as an act of invention, then it is also possible to argue that the artifacts it produces include language games and texts." (Karl E Weick, "Sensemaking in Organizations", 1995)

"When people perform an organized action sequence and are interrupted, they try to make sense of it. The longer they search, the higher the arousal, and the stronger the emotion. If the interruption slows the accomplishment of an organized sequence, people are likely to experience anger. If the interruption has accelerated accomplishment, then they are likely to experience pleasure. If people find that the interruption can be circumvented, they experience relief. If they find that the interruption has thwarted a higher level plan, then anger is likely to turn into rage, and if they find that the interruption has thwarted a minor behavioural sequence, they are likely to feel irritated." (Karl E Weick, "Sensemaking in Organizations", 1995)

"The basic idea of sensemaking is that reality is an ongoing accomplishment that emerges from efforts to create order and make retrospective sense of what occurs." (Karl E Weick, "The collapse of sensemaking in organizations: The Mann Gulch disaster", Administrative Science Quarterly 3, 1993)

13 September 2006

Carlos Gershenson - Collected Quotes

"Self-organization can be seen as a spontaneous coordination of the interactions between the components of the system, so as to maximize their synergy. This requires the propagation and processing of information, as different components perceive different aspects of the situation, while their shared goal requires this information to be integrated. The resulting process is characterized by distributed cognition: different components participate in different ways to the overall gathering and processing of information, thus collectively solving the problems posed by any perceived deviation between the present situation and the desired situation." (Carlos Gershenson & Francis Heylighen, "How can we think the complex?", 2004)

[synergy:] "Measure describing how one agent or system increases the satisfaction of other agents or systems." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"The second law of thermodynamics states that in an isolated system, entropy can only increase, not decrease. Such systems evolve to their state of maximum entropy, or thermodynamic equilibrium. Therefore, physical self-organizing systems cannot be isolated: they require a constant input of matter or energy with low entropy, getting rid of the internally generated entropy through the output of heat ('dissipation'). This allows them to produce ‘dissipative structures’ which maintain far from thermodynamic equilibrium. Life is a clear example of order far from thermodynamic equilibrium." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"Thus, nonlinearity can be understood as the effect of a causal loop, where effects or outputs are fed back into the causes or inputs of the process. Complex systems are characterized by networks of such causal loops. In a complex, the interdependencies are such that a component A will affect a component B, but B will in general also affect A, directly or indirectly.  A single feedback loop can be positive or negative. A positive feedback will amplify any variation in A, making it grow exponentially. The result is that the tiniest, microscopic difference between initial states can grow into macroscopically observable distinctions." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007) 

"To develop a Control, the designer should find aspect systems, subsystems, or constraints that will prevent the negative interferences between elements (friction) and promote positive interferences (synergy). In other words, the designer should search for ways of minimizing frictions that will result in maximization of the global satisfaction" (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"We have to be aware that even in mathematical and physical models of self-organizing systems, it is the observer who ascribes properties, aspects, states, and probabilities; and therefore entropy or order to the system. But organization is more than low entropy: it is structure that has a function or purpose." (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"Complexity carries with it a lack of predictability different to that of chaotic systems, i.e. sensitivity to initial conditions. In the case of complexity, the lack of predictability is due to relevant interactions and novel information created by them." (Carlos Gershenson, "Understanding Complex Systems", 2011)

"Complexity has shown that reductionism is limited, in the sense that emergent properties cannot be reduced. In other words, the properties at a given scale cannot be always described completely in terms of properties at a lower scale. This has led people to debate on the reality of phenomena at different scales." (Carlos Gershenson, "Complexity", 2011)

12 September 2006

Tim Harford - Collected Quotes

"An algorithm, meanwhile, is a step-by-step recipe for performing a series of actions, and in most cases 'algorithm' means simply 'computer program'." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Big data is revolutionizing the world around us, and it is easy to feel alienated by tales of computers handing down decisions made in ways we don’t understand. I think we’re right to be concerned. Modern data analytics can produce some miraculous results, but big data is often less trustworthy than small data. Small data can typically be scrutinized; big data tends to be locked away in the vaults of Silicon Valley. The simple statistical tools used to analyze small datasets are usually easy to check; pattern-recognizing algorithms can all too easily be mysterious and commercially sensitive black boxes." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Each decision about what data to gather and how to analyze them is akin to standing on a pathway as it forks left and right and deciding which way to go. What seems like a few simple choices can quickly multiply into a labyrinth of different possibilities. Make one combination of choices and you’ll reach one conclusion; make another, equally reasonable, and you might find a very different pattern in the data." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Each of us is sweating data, and those data are being mopped up and wrung out into oceans of information. Algorithms and large datasets are being used for everything from finding us love to deciding whether, if we are accused of a crime, we go to prison before the trial or are instead allowed to post bail. We all need to understand what these data are and how they can be exploited." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Good statistics are not a trick, although they are a kind of magic. Good statistics are not smoke and mirrors; in fact, they help us see more clearly. Good statistics are like a telescope for an astronomer, a microscope for a bacteriologist, or an X-ray for a radiologist. If we are willing to let them, good statistics help us see things about the world around us and about ourselves - both large and small - that we would not be able to see in any other way." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Ideally, a decision maker or a forecaster will combine the outside view and the inside view - or, similarly, statistics plus personal experience. But it’s much better to start with the statistical view, the outside view, and then modify it in the light of personal experience than it is to go the other way around. If you start with the inside view you have no real frame of reference, no sense of scale - and can easily come up with a probability that is ten times too large, or ten times too small." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"If we don’t understand the statistics, we’re likely to be badly mistaken about the way the world is. It is all too easy to convince ourselves that whatever we’ve seen with our own eyes is the whole truth; it isn’t. Understanding causation is tough even with good statistics, but hopeless without them. [...] And yet, if we understand only the statistics, we understand little. We need to be curious about the world that we see, hear, touch, and smell, as well as the world we can examine through a spreadsheet." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"[…] in a world where so many people seem to hold extreme views with strident certainty, you can deflate somebody’s overconfidence and moderate their politics simply by asking them to explain the details." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"It’d be nice to fondly imagine that high-quality statistics simply appear in a spreadsheet somewhere, divine providence from the numerical heavens. Yet any dataset begins with somebody deciding to collect the numbers. What numbers are and aren’t collected, what is and isn’t measured, and who is included or excluded are the result of all-too-human assumptions, preconceptions, and oversights." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Making big data work is harder than it seems. Statisticians have spent the past two hundred years figuring out what traps lie in wait when we try to understand the world through data. The data are bigger, faster, and cheaper these days, but we must not pretend that the traps have all been made safe. They have not." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Many people have strong intuitions about whether they would rather have a vital decision about them made by algorithms or humans. Some people are touchingly impressed by the capabilities of the algorithms; others have far too much faith in human judgment. The truth is that sometimes the algorithms will do better than the humans, and sometimes they won’t. If we want to avoid the problems and unlock the promise of big data, we’re going to need to assess the performance of the algorithms on a case-by-case basis. All too often, this is much harder than it should be. […] So the problem is not the algorithms, or the big datasets. The problem is a lack of scrutiny, transparency, and debate." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Much of the data visualization that bombards us today is decoration at best, and distraction or even disinformation at worst. The decorative function is surprisingly common, perhaps because the data visualization teams of many media organizations are part of the art departments. They are led by people whose skills and experience are not in statistics but in illustration or graphic design. The emphasis is on the visualization, not on the data. It is, above all, a picture." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Numbers can easily confuse us when they are unmoored from a clear definition." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Premature enumeration is an equal-opportunity blunder: the most numerate among us may be just as much at risk as those who find their heads spinning at the first mention of a fraction. Indeed, if you’re confident with numbers you may be more prone than most to slicing and dicing, correlating and regressing, normalizing and rebasing, effortlessly manipulating the numbers on the spreadsheet or in the statistical package - without ever realizing that you don’t fully understand what these abstract quantities refer to. Arguably this temptation lay at the root of the last financial crisis: the sophistication of mathematical risk models obscured the question of how, exactly, risks were being measured, and whether those measurements were something you’d really want to bet your global banking system on." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Sample error reflects the risk that, purely by chance, a randomly chosen sample of opinions does not reflect the true views of the population. The 'margin of error' reported in opinion polls reflects this risk, and the larger the sample, the smaller the margin of error. […] sampling error has a far more dangerous friend: sampling bias. Sampling error is when a randomly chosen sample doesn’t reflect the underlying population purely by chance; sampling bias is when the sample isn’t randomly chosen at all." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Statistical metrics can show us facts and trends that would be impossible to see in any other way, but often they’re used as a substitute for relevant experience, by managers or politicians without specific expertise or a close-up view." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Statisticians are sometimes dismissed as bean counters. The sneering term is misleading as well as unfair. Most of the concepts that matter in policy are not like beans; they are not merely difficult to count, but difficult to define. Once you’re sure what you mean by 'bean', the bean counting itself may come more easily. But if we don’t understand the definition, then there is little point in looking at the numbers. We have fooled ourselves before we have begun."(Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"So information is beautiful - but misinformation can be beautiful, too. And producing beautiful misinformation is becoming easier than ever." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"The contradiction between what we see with our own eyes and what the statistics claim can be very real. […] The truth is more complicated. Our personal experiences should not be dismissed along with our feelings, at least not without further thought. Sometimes the statistics give us a vastly better way to understand the world; sometimes they mislead us. We need to be wise enough to figure out when the statistics are in conflict with everyday experience - and in those cases, which to believe." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"The world is full of patterns that are too subtle or too rare to detect by eyeballing them, and a pattern doesn’t need to be very subtle or rare to be hard to spot without a statistical lens." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"The whole discipline of statistics is built on measuring or counting things. […] it is important to understand what is being measured or counted, and how. It is surprising how rarely we do this. Over the years, as I found myself trying to lead people out of statistical mazes week after week, I came to realize that many of the problems I encountered were because people had taken a wrong turn right at the start. They had dived into the mathematics of a statistical claim - asking about sampling errors and margins of error, debating if the number is rising or falling, believing, doubting, analyzing, dissecting - without taking the ti- me to understand the first and most obvious fact: What is being measured, or counted? What definition is being used?" (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Those of us in the business of communicating ideas need to go beyond the fact-check and the statistical smackdown. Facts are valuable things, and so is fact-checking. But if we really want people to understand complex issues, we need to engage their curiosity. If people are curious, they will learn." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"Unless we’re collecting data ourselves, there’s a limit to how much we can do to combat the problem of missing data. But we can and should remember to ask who or what might be missing from the data we’re being told about. Some missing numbers are obvious […]. Other omissions show up only when we take a close look at the claim in question." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"We don’t need to become emotionless processors of numerical information - just noticing our emotions and taking them into account may often be enough to improve our judgment. Rather than requiring superhuman control over our emotions, we need simply to develop good habits." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"We filter new information. If it accords with what we expect, we’ll be more likely to accept it. […] Our brains are always trying to make sense of the world around us based on incomplete information. The brain makes predictions about what it expects, and tends to fill in the gaps, often based on surprisingly sparse data." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"We should conclude nothing because that pair of numbers alone tells us very little. If we want to understand what’s happening, we need to step back and take in a broader perspective." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"[…] when it comes to interpreting the world around us, we need to realize that our feelings can trump our expertise. […] The more extreme the emotional reaction, the harder it is to think straight. […] It is not easy to master our emotions while assessing information that matters to us, not least because our emotions can lead us astray in different directions. […] We often find ways to dismiss evidence that we don’t like. And the opposite is true, too: when evidence seems to support our preconceptions, we are less likely to look too closely for flaws." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020)

"When we are trying to understand a statistical claim - any statistical claim - we need to start by asking ourselves what the claim actually means. [...] A surprising statistical claim is a challenge to our existing worldview. It may provoke an emotional response - even a fearful one." (Tim Harford, "The Data Detective: Ten easy rules to make sense of statistics", 2020) 

Nikola K Kasabov - Collected Quotes

"A strategy is usually expressed by a set of heuristic rules. The heuristic rules ease the process of searching for an optimal solution. The process is usually iterative and at one step either the global optimum for the whole problem (state) space is found and the process stops, or a local optimum for a subspace of the state space of the problem is found and the problem continues, if it is possible to improve." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Adaptation is the process of changing a system during its operation in a dynamically changing environment. Learning and interaction are elements of this process. Without adaptation there is no intelligence." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

 "An artificial neural network (or simply a neural network) is a biologically inspired computational model that consists of processing elements (neurons) and connections between them, as well as of training and recall algorithms." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Artificial intelligence comprises methods, tools, and systems for solving problems that normally require the intelligence of humans. The term intelligence is always defined as the ability to learn effectively, to react adaptively, to make proper decisions, to communicate in language or images in a sophisticated way, and to understand." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996) 

"Data obtained without any external disturbance or corruption are called clean; noisy data mean that a small random ingredient is added to the clean data." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Fuzzy systems are excellent tools for representing heuristic, commonsense rules. Fuzzy inference methods apply these rules to data and infer a solution. Neural networks are very efficient at learning heuristics from data. They are 'good problem solvers' when past data are available. Both fuzzy systems and neural networks are universal approximators in a sense, that is, for a given continuous objective function there will be a fuzzy system and a neural network which approximate it to any degree of accuracy." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Fuzzy systems are rule-based expert systems based on fuzzy rules and fuzzy inference. Fuzzy rules represent in a straightforward way 'commonsense' knowledge and skills, or knowledge that is subjective, ambiguous, vague, or contradictory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Generalization is the process of matching new, unknown input data with the problem knowledge in order to obtain the best possible solution, or one close to it. Generalization means reacting properly to new situations, for example, recognizing new images, or classifying new objects and situations. Generalization can also be described as a transition from a particular object description to a general concept description. This is a major characteristic of all intelligent systems." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996) 

"Generally speaking, problem knowledge for solving a given problem may consist of heuristic rules or formulas that comprise the explicit knowledge, and past-experience data that comprise the implicit, hidden knowledge. Knowledge represents links between the domain space and the solution space, the space of the independent variables and the space of the dependent variables." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Heuristic (it is of Greek origin) means discovery. Heuristic methods are based on experience, rational ideas, and rules of thumb. Heuristics are based more on common sense than on mathematics. Heuristics are useful, for example, when the optimal solution needs an exhaustive search that is not realistic in terms of time. In principle, a heuristic does not guarantee the best solution, but a heuristic solution can provide a tremendous shortcut in cost and time." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Heuristic methods may aim at local optimization rather than at global optimization, that is, the algorithm optimizes the solution stepwise, finding the best solution at each small step of the solution process and 'hoping' that the global solution, which comprises the local ones, would be satisfactory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Inference is the process of matching current facts from the domain space to the existing knowledge and inferring new facts. An inference process is a chain of matchings. The intermediate results obtained during the inference process are matched against the existing knowledge. The length of the chain is different. It depends on the knowledge base and on the inference method applied." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Learning is the process of obtaining new knowledge. It results in a better reaction to the same inputs at the next session of operation. It means improvement. It is a step toward adaptation. Learning is a major characteristic of intelligent systems." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Prediction (forecasting) is the process of generating information for the possible future development of a process from data about its past and its present development." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Representation is the process of transforming existing problem knowledge to some of the known knowledge-engineering schemes in order to process it by applying knowledge-engineering methods. The result of the representation process is the problem knowledge base in a computer format." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"The most distinguishing property of fuzzy logic is that it deals with fuzzy propositions, that is, propositions which contain fuzzy variables and fuzzy values, for example, 'the temperature is high', 'the height is short'. The truth values for fuzzy propositions are not TRUE/FALSE only, as is the case in propositional boolean logic, but include all the grayness between two extreme values." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Validation is the process of testing how good the solutions produced by a system are. The results produced by a system are usually compared with the results obtained either by experts or by other systems. Validation is an extremely important part of the process of developing every knowledge-based system. Without comparing the results produced by the system with reality, there is little point in using it." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

11 September 2006

Matthew Kirk - Collected Quotes

"A good proxy for complexity in a machine learning model is how fast it takes to train it." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"Cross-validation is a method of splitting all of your data into two parts: training and validation. The training data is used to build the machine learning model, whereas the validation data is used to validate that the model is doing what is expected. This increases our ability to find and determine the underlying errors in a model." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"In statistics, there is a measure called power that denotes the probability of not finding a false negative. As power goes up, false negatives go down. However, what influences this measure is the sample size. If our sample size is too small, we just don’t have enough information to come up with a good solution." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"Machine learning is a science and requires an objective approach to problems. Just like the scientific method, test-driven development can aid in solving a problem. The reason that TDD and the scientific method are so similar is because of these three shared characteristics: Both propose that the solution is logical and valid. Both share results through documentation and work over time. Both work in feedback loops." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"Machine learning is the intersection between theoretically sound computer science and practically noisy data. Essentially, it’s about machines making sense out of data in much the same way that humans do." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"Machine learning is well suited for the unpredictable future, because most algorithms learn from new information. But as new information is found, it can also come in unstable forms, and new issues can arise that weren’t thought of before. We don’t know what we don’t know. When processing new information, it’s sometimes hard to tell whether our model is working." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"Precision and recall are ways of monitoring the power of the machine learning implementation. Precision is a metric that monitors the percentage of true positives. […] Recall is the ratio of true positives to true positive plus false negatives." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

"Supervised learning, or function approximation, is simply fitting data to a function of any variety.  […] Unsupervised learning involves figuring out what makes the data special. […] Reinforcement learning involves figuring out how to play a multistage game with rewards and payoffs. Think of it as the algorithms that optimize the life of something." (Matthew Kirk, "Thoughtful Machine Learning", 2015)

09 September 2006

Scott E Page - Collected Quotes

"Effective models require a real world that has enough structure so that some of the details can be ignored. This implies the existence of solid and stable building blocks that encapsulate key parts of the real system’s behavior. Such building blocks provide enough separation from details to allow modeling to proceed." (John H Miller & Scott E Page, "Complex Adaptive Systems: An Introduction to Computational Models of Social Life", 2007)

"Models need to be judged by what they eliminate as much as by what they include - like stone carving, the art is in removing what you do not need." (John H Miller & Scott E Page, "Complex Adaptive Systems: An Introduction to Computational Models of Social Life", 2007)

"A heuristic is a rule applied to an existing solution represented in a perspective that generates a new (and hopefully better) solution or a new set of possible solutions." (Scott E Page, "The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies", 2008)

"A perspective is a map from reality to an internal language such that each distinct object, situation, problem, or event gets mapped to a unique word." (Scott E Page, "The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies", 2008)

"[...] diverse, connected, interdependent entities whose behavior is determined by rules, which may adapt, but need not. The interactions of these entities often produce phenomena that are more than the parts. These phenomena are called emergent." (Scott E Page, "Diversity and Complexity", 2010)

"If we can understand how to leverage diversity to achieve better performance and greater robustness, we might anticipate and prevent collapses." (Scott E Page, "Diversity and Complexity", 2010)

"[…] many-model thinking produces wisdom through a diverse ensemble of logical frames. The various models accentuate different causal forces. Their insights and implications overlap and interweave. By engaging many models as frames, we develop nuanced, deep understandings." (Scott E Page," The Model Thinker", 2018)

“Models are formal structures represented in mathematics and diagrams that help us to understand the world. Mastery of models improves your ability to reason, explain, design, communicate, act, predict, and explore.” (Scott E Page, “The Model Thinker”, 2018)

"Collective intelligence is where the whole is smarter than any one individual in it. You can think of it that in a predictive context, this could be the wisdom of crowds, sort of thing where people guessing the weight of a steer, the crowd’s guess is going to be better than the average guess of the person in it." (Scott E Page [interview])

"Diverse groups of problem solvers outperformed the groups of the best individuals at solving complex problems. The reason: the diverse groups got stuck less often than the smart individuals, who tended to think similarly." (Scott E Page)

07 September 2006

Philip Kotler - Collected Quotes

"Good mission statements focus on a limited number of goals, stress the company's major policies and values, and define the company's major competitive scopes." (Philip Kotler, "Marketing Management: Analysis, Planning, Implementation and Control", 1967)

"Marketing is not the art of finding clever ways to dispose of what you make. It is the art of creating genuine customer value. It is the art of helping your customers become better of." (Philip Kotler, "Marketing Management: Analysis, Planning, Implementation and Control", 1967)

"Marketing management is the analysis, planning, implementation, and control of programs designed to create, build, and maintain beneficial exchanges with target buyers for the purpose of achieving organizational objectives." (Philip Kotler, "Strategic Management: Analysis, Planning, Implementation and Control", 1993)

"Companies pay too much attention to the cost of doing something. They should worry more about the cost of not doing it." (Philip Kotler, "Marketing Insights from A to Z: 80 Concepts Every Manager Needs to Know", 2003)

"A clear, thoughtful mission statement, developed collaboratively with and shared with managers, employees, and often customers, provides a shared sense of purpose, direction, and opportunity." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"Culture is the fundamental determinant of a person’s wants and behavior." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"Goals indicate what a business unit wants to achieve; strategy is a game plan for getting there. Every business must design a strategy for achieving its goals, consisting of a marketing strategy and a compatible technology strategy and sourcing strategy." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"Good mission statements have five major characteristics. (1) They focus on a limited number of goals. (2) They stress the company’s major policies and values. (3) They define the major competitive spheres within which the company will operate. (4) They take a long-term view. (5) They are as short, memorable, and meaningful as possible." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"Strategic planning happens within the context of the organization. A company’s organization consists of its structures, policies, and corporate culture, all of which can become dysfunctional in a rapidly changing business environment. Whereas managers can change structures and policies (though with difficulty), the company’s culture is very hard to change. Yet adapting the culture is often the key to successfully implementing a new strategy." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

"To ensure they execute the right activities, marketers must prioritize strategic planning in three key areas: (1) managing the businesses as an investment portfolio, (2) assessing the market’s growth rate and the company’s position in that market, and (3) establishing a strategy. The company must develop a game plan for achieving each business’s long-run objectives." (Philip Kotler & Kevin L Keller, "Marketing Management" 15th Ed., 2016)

06 September 2006

Beau Lotto - Collected Quotes

"Effects without an understanding of the causes behind them, on the other hand, are just bunches of data points floating in the ether, offering nothing useful by themselves. Big Data is information, equivalent to the patterns of light that fall onto the eye. Big Data is like the history of stimuli that our eyes have responded to. And as we discussed earlier, stimuli are themselves meaningless because they could mean anything. The same is true for Big Data, unless something transformative is brought to all those data sets… understanding." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"New information is constantly flowing in, and your brain is constantly integrating it into this statistical distribution that creates your next perception (so in this sense 'reality' is just the product of your brain’s ever-evolving database of consequence). As such, your perception is subject to a statistical phenomenon known in probability theory as kurtosis. Kurtosis in essence means that things tend to become increasingly steep in their distribution… that is, skewed in one direction. This applies to ways of seeing everything from current events to ourselves as we lean 'skewedly' toward one interpretation, positive or negative. Things that are highly kurtotic, or skewed, are hard to shift away from. This is another way of saying that seeing differently isn’t just conceptually difficult - it’s statistically difficult." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"Our assumptions are un question ably interconnected. They are nodes with connections (edges) to other nodes. The more foundational the assumption, the more strongly connected it is. What I’m suggesting is that our assumptions and the highly sensitive network of responses, perceptions, behaviors, thoughts, and ideas they create and interact with are a complex system. One of the most basic features of such a network is that when you move or disrupt one thing that is strongly connected, you don’t just affect that one thing, you affect all the other things that are connected to it. Hence small causes can have massive effects (but they don’t have to, and usually don’t actually). In a system of high tension, simple questions targeting basic assumptions have the potential to transform perception in radical  and unpredictable ways." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"Questioning our assumptions is what provokes revolutions, be they tiny or vast, technological or social." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"Understanding reduces the complexity of data by collapsing the dimensionality of information to a lower set of known variables. s revolutions, be they tiny or vast, technological or social." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"The basis of complex systems is actually quite simple (and this is not an attempt to be paradoxical, like an art critic who describes a sculpture as 'big yet small'). What makes a system unpredictable and thus nonlinear (which includes you and your perceptual process, or the process of making collective decisions) is that the components making up the system are interconnected." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"The greatest leaders possess a combination of divergent traits: they are both experts and naïve, creative and efficient, serious and playful, social and reclusive - or at the very least, they surround themselves with this dynamic." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017) 

"The term [Big Data] simply refers to sets of data so immense that they require new methods of mathematical analysis, and numerous servers. Big Data - and, more accurately, the capacity to collect it - has changed the way companies conduct business and governments look at problems, since the belief wildly trumpeted in the media is that this vast repository of information will yield deep insights that were previously out of reach." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"Trust is fundamental to leading others into the dark, since trust enables fear to be 'actionable' as courage rather than actionable as anger. Since the bedrock of trust is faith that all will be OK within uncertainty, leaders’ fundamental role is to ultimately lead themselves. Research has found that successful leaders share three behavioral traits: they lead by example, admit their mistakes, and see positive qualities in others. All three are linked to spaces of play. Leading by example creates a space that is trusted - and without trust, there is no play. Admitting mistakes is to celebrate uncertainty. Seeing qualities in others is to encourage diversity." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"Understanding transcends context, since the different contexts collapse according to their previously unknown similarity, which the principle contains. That is what understanding does. And you actually feel it in your brain when it happens. Your 'cognitive load' decreases, your level of stress and anxiety decrease, and your emotional state improves." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

"What defines a good leader? Enabling other people to step into the unseen. […] as the world becomes increasingly connected and thus unpredictable, the concept of leadership too must change. Rather than lead from the front toward efficiency, offering the answers, a good leader is defined by how he or she leads others into darkness - into uncertainty." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)

05 September 2006

Mike Loukides - Collected Quotes

"Data is frequently missing or incongruous. If data is missing, do you simply ignore the missing points? That isn’t always possible. If data is incongruous, do you decide that something is wrong with badly behaved data (after all, equipment fails), or that the incongruous data is telling its own story, which may be more interesting? It’s reported that the discovery of ozone layer depletion was delayed because automated data collection tools discarded readings that were too low. In data science, what you have is frequently all you’re going to get. It’s usually impossible to get 'better' data, and you have no alternative but to work with the data at hand." (Mike Loukides, "What Is Data Science?", 2011).

"Data science isn’t just about the existence of data, or making guesses about what that data might mean; it’s about testing hypotheses and making sure that the conclusions you’re drawing from the data are valid." (Mike Loukides, "What Is Data Science?", 2011)

"Data scientists combine entrepreneurship with patience, the willingness to build data products incrementally, the ability to explore, and the ability to iterate over a solution. They are inherently interdisciplinary. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: 'there’s a lot of data, what can you make from it?'" (Mike Loukides, "What Is Data Science?", 2011)

"Discovery is the key to building great data products, as opposed to products that are merely good." (Mike Loukides, "The Evolution of Data Products", 2011)

"New interfaces for data products are all about hiding the data itself, and getting to what the user wants." (Mike Loukides, "The Evolution of Data Products", 2011)

"The thread that ties most of these applications together is that data collected from users provides added value. Whether that data is search terms, voice samples, or product reviews, the users are in a feedback loop in which they contribute to the products they use. That’s the beginning of data science." (Mike Loukides, "What Is Data Science?", 2011)

"Using data effectively requires something different from traditional statistics, where actuaries in business suits perform arcane but fairly well-defined kinds of analysis. What differentiates data science from statistics is that data science is a holistic approach. We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others" (Mike Loukides, "What Is Data Science?", 2011).

"Whether we’re talking about web server logs, tweet streams, online transaction records, 'citizen science', data from sensors, government data, or some other source, the problem isn’t finding data, it’s figuring out what to do with it." (Mike Loukides, "What Is Data Science?", 2011)

Charles Wheelan - Collected Quotes

"A statistical index has all the potential pitfalls of any descriptive statistic - plus the distortions introduced by combining multiple indicators into a single number. By definition, any index is going to be sensitive to how it is constructed; it will be affected both by what measures go into the index and by how each of those measures is weighted." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Correlation measures the degree to which two phenomena are related to one another. [...] Two variables are positively correlated if a change in one is associated with a change in the other in the same direction, such as the relationship between height and weight. [...] A correlation is negative if a positive change in one variable is associated with a negative change in the other, such as the relationship between exercise and weight." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Descriptive statistics give us insight into phenomena that we care about. […] Although the field of statistics is rooted in mathematics, and mathematics is exact, the use of statistics to describe complex phenomena is not exact. That leaves plenty of room for shading the truth." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Even if you have a solid indicator of what you are trying to measure and manage, the challenges are not over. The good news is that 'managing by statistics' can change the underlying behavior of the person or institution being managed for the better. If you can measure the proportion of defective products coming off an assembly line, and if those defects are a function of things happening at the plant, then some kind of bonus for workers that is tied to a reduction in defective products would presumably change behavior in the right kinds of ways. Each of us responds to incentives (even if it is just praise or a better parking spot). Statistics measure the outcomes that matter; incentives give us a reason to improve those outcomes." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Even in the best of circumstances, statistical analysis rarely unveils “the truth.” We are usually building a circumstantial case based on imperfect data. As a result, there are numerous reasons that intellectually honest individuals may disagree about statistical results or their implications. At the most basic level, we may disagree on the question that is being answered." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"If the distance from the mean for one variable tends to be broadly consistent with distance from the mean for the other variable (e.g., people who are far from the mean for height in either direction tend also to be far from the mean in the same direction for weight), then we would expect a strong positive correlation. If distance from the mean for one variable tends to correspond to a similar distance from the mean for the second variable in the other direction (e.g., people who are far above the mean in terms of exercise tend to be far below the mean in terms of weight), then we would expect a strong negative correlation. If two variables do not tend to deviate from the mean in any meaningful pattern (e.g., shoe size and exercise) then we would expect little or no correlation." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Once these different measures of performance are consolidated into a single number, that statistic can be used to make comparisons […] The advantage of any index is that it consolidates lots of complex information into a single number. We can then rank things that otherwise defy simple comparison […] Any index is highly sensitive to the descriptive statistics that are cobbled together to build it, and to the weight given to each of those components. As a result, indices range from useful but imperfect tools to complete charades." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Probability is the study of events and outcomes involving an element of uncertainty." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Regression analysis, like all forms of statistical inference, is designed to offer us insights into the world around us. We seek patterns that will hold true for the larger population. However, our results are valid only for a population that is similar to the sample on which the analysis has been done." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"Statistics cannot be any smarter than the people who use them. And in some cases, they can make smart people do dumb things." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"The correlation coefficient has two fabulously attractive characteristics. First, for math reasons that have been relegated to the appendix, it is a single number ranging from –1 to 1. A correlation of 1, often described as perfect correlation, means that every change in one variable is associated with an equivalent change in the other variable in the same direction. A correlation of –1, or perfect negative correlation, means that every change in one variable is associated with an equivalent change in the other variable in the opposite direction. The closer the correlation is to 1 or –1, the stronger the association. […] The second attractive feature of the correlation coefficient is that it has no units attached to it. […] The correlation coefficient does a seemingly miraculous thing: It collapses a complex mess of data measured in different units (like our scatter plots of height and weight) into a single, elegant descriptive statistic." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"The problem is that the mechanics of regression analysis are not the hard part; the hard part is determining which variables ought to be considered in the analysis and how that can best be done. Regression analysis is like one of those fancy power tools. It is relatively easy to use, but hard to use well - and potentially dangerous when used improperly." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"There are limits on the data we can gather and the kinds of experiments we can perform."(Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

"While the main point of statistics is to present a meaningful picture of things we care about, in many cases we also hope to act on these numbers." (Charles Wheelan, "Naked Statistics: Stripping the Dread from the Data", 2012)

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