✏️Jose Berengueres - Collected Quotes

"[...] a mark of due diligence is to always ask if there is more data." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Any good set of data will offer transparency into the methodology of how the data was gathered. This means paying particular attention to what and how questions are asked in surveys or statements made. A red flag is any use of adverbs and adjectives. They are usually loaded with bias." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Bias not only can be sorted by their point of entry (data, story, narrative) but also by the area they exploit in the cognition system (optical illusions, cultural biases). It is easy to assume that bias is intentional. However, bias can emerge for many reasons. First, bias can be embedded in the data itself, intentionally in the way it is gathered but also accidentally by not realizing what is missing. Second, bias can appear as the story is crafted. Again, this can be intentional by cherry-picking from existing data, or accidental from cases where not enough time is spent exploring all data available (usually due to time pressure). Third, it can be embedded in the narrative itself." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Helping the reader situate the new information into existing frameworks makes the new information easier to assimilate, use and recall." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"In broad terms, bias is any systematic error. In other words, a systematic difference between a model and the 'truth' it supposedly represents. In social sciences, bias is judged to be unethical when it is unfair (usually towards a minority)." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Mind the gap is a common strategy to think about differences between categories in the data [...]. Thinking about why the gap exists can help explain the reality that the chart is representing." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Note how the key step to creating meaning (knowledge) is not only to summarize and declutter but to find where the information is most useful and then by linking it to that context (reference framework)." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"There is a fundamental difference between circular charts and bar charts. The brain is sensitive to angular change and (by comparison) quite numb to linear change. This is particularly true when considering motion, and sensitivity to small changes. If in your narrative, highlighting minute changes in a variable is important for the story, then circular pie charts (speed needle gauges) are the way to go. If on the contrary, too much attention to change is a distraction, avoid pie charts and needles."(Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Unfortunately, aesthetically pleasing visuals and a visual that gets the job done do not always coincide." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

"Unless you are in a preliminary Exploratory Data Analysis (EDA), it is not a good idea to disseminate a chart unless there is a clear why (narrative) for the chart. And even if you produce many charts as a part of an EDA, resist the temptation to show them off." (Jose Berengueres & Marybeth Sandell, "Introduction to Data Visualization & Storytelling: A Guide For The Data Scientist" 2nd. Ed., 2019)

🔭Data Science: Location (Just the Quotes)

"There are several reasons why symmetry is an important concept in data analysis. First, the most important single summary of a set of data is the location of the center, and when data meaning of 'center' is unambiguous. We can take center to mean any of the following things, since they all coincide exactly for symmetric data, and they are together for nearly symmetric data: (l) the center of symmetry. (2) the arithmetic average or center of gravity, (3) the median or 50%. Furthermore, if data a single point of highest concentration instead of several (that is, they are unimodal), then we can add to the list (4) point of highest concentration. When data are far from symmetric, we may have trouble even agreeing on what we mean by center; in fact, the center may become an inappropriate summary for the data." (John M Chambers et al,Graphical Methods for Data Analysis", 1983)

"Data that are skewed toward large values occur commonly. Any set of positive measurements is a candidate. Nature just works like that. In fact, if data consisting of positive numbers range over several powers of ten, it is almost a guarantee that they will be skewed. Skewness creates many problems. There are visualization problems. A large fraction of the data are squashed into small regions of graphs, and visual assessment of the data degrades. There are characterization problems. Skewed distributions tend to be more complicated than symmetric ones; for example, there is no unique notion of location and the median and mean measure different aspects of the distribution. There are problems in carrying out probabilistic methods. The distribution of skewed data is not well approximated by the normal, so the many probabilistic methods based on an assumption of a normal distribution cannot be applied." (William S Cleveland,Visualizing Data", 1993)

"Fitting data means finding mathematical descriptions of structure in the data. An additive shift is a structural property of univariate data in which distributions differ only in location and not in spread or shape. […] The process of identifying a structure in data and then fitting the structure to produce residuals that have the same distribution lies at the heart of statistical analysis. Such homogeneous residuals can be pooled, which increases the power of the description of the variation in the data." (William S Cleveland,Visualizing Data", 1993)

"When the distributions of two or more groups of univariate data are skewed, it is common to have the spread increase monotonically with location. This behavior is monotone spread. Strictly speaking, monotone spread includes the case where the spread decreases monotonically with location, but such a decrease is much less common for raw data. Monotone spread, as with skewness, adds to the difficulty of data analysis. For example, it means that we cannot fit just location estimates to produce homogeneous residuals; we must fit spread estimates as well. Furthermore, the distributions cannot be compared by a number of standard methods of probabilistic inference that are based on an assumption of equal spreads; the standard t-test is one example. Fortunately, remedies for skewness can cure monotone spread as well." (William S Cleveland,Visualizing Data", 1993)

"Since the average is a measure of location, it is common to use averages to compare two data sets. The set with the greater average is thought to ‘exceed’ the other set. While such comparisons may be helpful, they must be used with caution. After all, for any given data set, most of the values will not be equal to the average." (Donald J Wheeler,Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Distinguish among confidence, prediction, and tolerance intervals. Confidence intervals are statements about population means or other parameters. Prediction intervals address future" (single or multiple) observations. Tolerance intervals describe the location of a specific proportion of a population, with specified confidence." (Gerald van Belle,Statistical Rules of Thumb", 2002)

"If the sample is not representative of the population because the sample is small or biased, not selected at random, or its constituents are not independent of one another, then the bootstrap will fail. […] For a given size sample, bootstrap estimates of percentiles in the tails will always be less accurate than estimates of more centrally located percentiles. Similarly, bootstrap interval estimates for the variance of a distribution will always be less accurate than estimates of central location such as the mean or median because the variance depends strongly upon extreme values in the population." (Phillip I Good & James W Hardin,Common Errors in Statistics" (and How to Avoid Them)", 2003)

"The central limit theorem is often used to justify the assumption of normality when using the sample mean and the sample standard deviation. But it is inevitable that real data contain gross errors. Five to ten percent unusual values in a dataset seem to be the rule rather than the exception. The distribution of such data is no longer Normal." (A S Hedayat & Guoqin Su,Robustness of the Simultaneous Estimators of Location and Scale From Approximating a Histogram by a Normal Density Curve", The American Statistician 66, 2012)

🔭Data Science: Guessing (Just the Quotes)

"Summing up, then, it would seem as if the mind of the great discoverer must combine contradictory attributes. He must be fertile in theories and hypotheses, and yet full of facts and precise results of experience. He must entertain the feeblest analogies, and the merest guesses at truth, and yet he must hold them as worthless till they are verified in experiment. When there are any grounds of probability he must hold tenaciously to an old opinion, and yet he must be prepared at any moment to relinquish it when a clearly contradictory fact is encountered." (William S Jevons,The Principles of Science: A Treatise on Logic and Scientific Method", 1874)

"It would be an error to suppose that the great discoverer seizes at once upon the truth, or has any unerring method of divining it. In all probability the errors of the great mind exceed in number those of the less vigorous one. Fertility of imagination and abundance of guesses at truth are among the first requisites of discovery; but the erroneous guesses must be many times as numerous as those that prove well founded. The weakest analogies, the most whimsical notions, the most apparently absurd theories, may pass through the teeming brain, and no record remain of more than the hundredth part. […] The truest theories involve suppositions which are inconceivable, and no limit can really be placed to the freedom of hypotheses." (W Stanley Jevons,The Principles of Science: A Treatise on Logic and Scientific Method", 1877)

"Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building." (George Pólya,How to Solve It", 1945)

"The scientist who discovers a theory is usually guided to his discovery by guesses; he cannot name a method by means of which he found the theory and can only say that it appeared plausible to him, that he had the right hunch or that he saw intuitively which assumption would fit the facts." (Hans Reichenbach,The Rise of Scientific Philosophy", 1951)

"Extrapolations are useful, particularly in the form of soothsaying called forecasting trends. But in looking at the figures or the charts made from them, it is necessary to remember one thing constantly: The trend to now may be a fact, but the future trend represents no more than an educated guess. Implicit in it is 'everything else being equal' and 'present trends continuing'. And somehow everything else refuses to remain equal." (Darell Huff,How to Lie with Statistics", 1954)

"In plausible reasoning the principal thing is to distinguish... a more reasonable guess from a less reasonable guess." (George Pólya,Mathematics and plausible reasoning" Vol. 1, 1954)

"We know many laws of nature and we hope and expect to discover more. Nobody can foresee the next such law that will be discovered. Nevertheless, there is a structure in laws of nature which we call the laws of invariance. This structure is so far-reaching in some cases that laws of nature were guessed on the basis of the postulate that they fit into the invariance structure." (Eugene P Wigner,The Role of Invariance Principles in Natural Philosophy", 1963)

"Another thing I must point out is that you cannot prove a vague theory wrong. If the guess that you make is poorly expressed and rather vague, and the method that you use for figuring out the consequences is a little vague - you are not sure, and you say, 'I think everything's right because it's all due to so and so, and such and such do this and that more or less, and I can sort of explain how this works' […] then you see that this theory is good, because it cannot be proved wrong! Also if the process of computing the consequences is indefinite, then with a little skill any experimental results can be made to look like the expected consequences." (Richard P Feynman,The Character of Physical Law", 1965)

"The method of guessing the equation seems to be a pretty effective way of guessing new laws. This shows again that mathematics is a deep way of expressing nature, and any attempt to express nature in philosophical principles, or in seat-of-the-pants mechanical feelings, is not an efficient way." (Richard Feynman,The Character of Physical Law", 1965)

"Every discovery, every enlargement of the understanding, begins as an imaginative preconception of what the truth might be. The imaginative preconception - a ‘hypothesis’ - arises by a process as easy or as difficult to understand as any other creative act of mind; it is a brainwave, an inspired guess, a product of a blaze of insight. It comes anyway from within and cannot be achieved by the exercise of any known calculus of discovery." (Sir Peter B Medawar,Advice to a Young Scientist", 1979)

"Scientists reach their  conclusions  for the damnedest of reasons: intuition, guesses, redirections after wild-goose chases, all combing with a dollop of rigorous observation and logical  reasoning to be sure […] This  messy and personal side of science should not be  disparaged, or covered up, by  scientists for two  major reasons. First, scientists should proudly show this  human face to  display their kinship with all other  modes of creative human thought […] Second, while biases and references often impede understanding, these  mental idiosyncrasies  may  also serve as powerful, if  quirky and personal, guides to solutions." (Stephen J Gould,Dinosaur in a  Haystack: Reflections in natural  history", 1995)

"Compound errors can begin with any of the standard sorts of bad statistics - a guess, a poor sample, an inadvertent transformation, perhaps confusion over the meaning of a complex statistic. People inevitably want to put statistics to use, to explore a number's implications. [...] The strengths and weaknesses of those original numbers should affect our confidence in the second-generation statistics." (Joel Best,Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"First, good statistics are based on more than guessing. [...] Second, good statistics are based on clear, reasonable definitions. Remember, every statistic has to define its subject. Those definitions ought to be clear and made public. [...] Third, good statistics are based on clear, reasonable measures. Again, every statistic involves some sort of measurement; while all measures are imperfect, not all flaws are equally serious. [...] Finally, good statistics are based on good samples." (Joel Best,Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"While some social problems statistics are deliberate deceptions, many - probably the great majority - of bad statistics are the result of confusion, incompetence, innumeracy, or selective, self-righteous efforts to produce numbers that reaffirm principles and interests that their advocates consider just and right. The best response to stat wars is not to try and guess who's lying or, worse, simply to assume that the people we disagree with are the ones telling lies. Rather, we need to watch for the standard causes of bad statistics - guessing, questionable definitions or methods, mutant numbers, and inappropriate comparisons." (Joel Best,Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)

"The well-known 'No Free Lunch' theorem indicates that there does not exist a pattern classification method that is inherently superior to any other, or even to random guessing without using additional information. It is the type of problem, prior information, and the amount of training samples that determine the form of classifier to apply. In fact, corresponding to different real-world problems, different classes may have different underlying data structures. A classifier should adjust the discriminant boundaries to fit the structures which are vital for classification, especially for the generalization capacity of the classifier." (Hui Xue et al,SVM: Support Vector Machines", 2009)

"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)

"GIGO is a famous saying coined by early computer scientists: garbage in, garbage out. At the time, people would blindly put their trust into anything a computer output indicated because the output had the illusion of precision and certainty. If a statistic is composed of a series of poorly defined measures, guesses, misunderstandings, oversimplifications, mismeasurements, or flawed estimates, the resulting conclusion will be flawed." (Daniel J Levitin,Weaponized Lies", 2017)

"In statistical inference and machine learning, we often talk about estimates and estimators. Estimates are basically our best guesses regarding some quantities of interest given" (finite) data. Estimators are computational devices or procedures that allow us to map between a given" (finite) data sample and an estimate of interest." (Aleksander Molak,Causal Inference and Discovery in Python", 2023)

🔭Data Science: Heuristics (Just the Quotes)

"Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building." (George Pólya,How to Solve It", 1945)

"The attempt to characterize exactly models of an empirical theory almost inevitably yields a more precise and clearer understanding of the exact character of a theory. The emptiness and shallowness of many classical theories in the social sciences is well brought out by the attempt to formulate in any exact fashion what constitutes a model of the theory. The kind of theory which mainly consists of insightful remarks and heuristic slogans will not be amenable to this treatment. The effort to make it exact will at the same time reveal the weakness of the theory." (Patrick Suppes," A Comparison of the Meaning and Uses of Models in Mathematics and the Empirical Sciences", Synthese  Vol. 12" (2/3), 1960)

"Design problems - generating or discovering alternatives - are complex largely because they involve two spaces, an action space and a state space, that generally have completely different structures. To find a design requires mapping the former of these on the latter. For many, if not most, design problems in the real world systematic algorithms are not known that guarantee solutions with reasonable amounts of computing effort. Design uses a wide range of heuristic devices - like means-end analysis, satisficing, and the other procedures that have been outlined - that have been found by experience to enhance the efficiency of search. Much remains to be learned about the nature and effectiveness of these devices." (Herbert A Simon,The Logic of Heuristic Decision Making", [inThe Logic of Decision and Action"], 1966)

"Intelligence has two parts, which we shall call the epistemological and the heuristic. The epistemological part is the representation of the world in such a form that the solution of problems follows from the facts expressed in the representation. The heuristic part is the mechanism that on the basis of the information solves the problem and decides what to do." (John McCarthy & Patrick J Hayes,Some Philosophical Problems from the Standpoint of Artificial Intelligence", Machine Intelligence 4, 1969)

"Consider any of the heuristics that people have come up with for supervised learning: avoid overfitting, prefer simpler to more complex models, boost your algorithm, bag it, etc. The no free lunch theorems say that all such heuristics fail as often" (appropriately weighted) as they succeed. This is true despite formal arguments some have offered trying to prove the validity of some of these heuristics." (David H Wolpert,The lack of a priori distinctions between learning algorithms", Neural Computation Vol. 8(7), 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)

"Theories of choice are at best approximate and incomplete. One reason for this pessimistic assessment is that choice is a constructive and contingent process. When faced with a complex problem, people employ a variety of heuristic procedures in order to simplify the representation and the evaluation of prospects. These procedures include computational shortcuts and editing operations, such as eliminating common components and discarding nonessential differences. The heuristics of choice do not readily lend themselves to formal analysis because their application depends on the formulation of the problem, the method of elicitation, and the context of choice." (Amos Tversky & Daniel Kahneman,Advances in Prospect Theory: Cumulative Representation of Uncertainty" [inChoices, Values, and Frames"], 2000)

"Behavioural research shows that we tend to use simplifying heuristics when making judgements about uncertain events. These are prone to biases and systematic errors, such as stereotyping, disregard of sample size, disregard for regression to the mean, deriving estimates based on the ease of retrieving instances of the event, anchoring to the initial frame, the gambler’s fallacy, and wishful thinking, which are all affected by our inability to consider more than a few aspects or dimensions of any phenomenon or situation at the same time." (Hans G Daellenbach & Donald C McNickle,Management Science: Decision making through systems thinking", 2005)

"A decision theory that rests on the assumptions that human cognitive capabilities are limited and that these limitations are adaptive with respect to the decision environments humans frequently encounter. Decision are thought to be made usually without elaborate calculations, but instead by using fast and frugal heuristics. These heuristics certainly have the advantage of speed and simplicity, but if they are well matched to a decision environment, they can even outperform maximizing calculations with respect to accuracy. The reason for this is that many decision environments are characterized by incomplete information and noise. The information we do have is usually structured in a specific way that clever heuristics can exploit." (E Ebenhoh,Agent-Based Modelnig with Boundedly Rational Agents", 2007)

"Optimization systems (or optimizers, as they are often referred to) aim to optimize in a systematic way, oftentimes using a heuristics-based approach. Such an approach enables the AI system to use a macro level concept as part of its low-level calculations, accelerating the whole process and making it more light-weight. After all, most of these systems are designed with scalability in mind, so the heuristic approach is most practical." (Yunus E Bulut & Zacharias Voulgaris,AI for Data Science: Artificial Intelligence Frameworks and Functionality for Deep Learning, Optimization, and Beyond", 2018)

"The social world that humans have made for themselves is so complex that the mind simplifies the world by using heuristics, customs, and habits, and by making models or assumptions about how things generally work (the ‘causal structure of the world’). And because people rely upon" (and are invested in) these mental models, they usually prefer that they remain uncontested." (Dr James Brennan,Psychological  Adjustment to Illness and Injury", West of England Medical Journal Vol. 117 (2), 2018)

"Many AI systems employ heuristic decision making, which uses a strategy to find the most likely correct decision to avoid the high cost" (time) of processing lots of information. We can think of those heuristics as shortcuts or rules of thumb that we would use to make fast decisions." (Jesús Barrasa et al,Knowledge Graphs: Data in Context for Responsive Businesses", 2021)

"Once we know something is fat-tailed, we can use heuristics to see how an exposure there reacts to random events: how much is a given unit harmed by them. It is vastly more effective to focus on being insulated from the harm of random events than try to figure them out in the required details" (as we saw the inferential errors under thick tails are huge). So it is more solid, much wiser, more ethical, and more effective to focus on detection heuristics and policies rather than fabricate statistical properties." (Nassim N Taleb,Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

🔭Data Science: Tails (Just the Quotes)

"Some distributions [...] are symmetrical about their central value. Other distributions have marked asymmetry and are said to be skew. Skew distributions are divided into two types. If the 'tail' of the distribution reaches out into the larger values of the variate, the distribution is said to show positive skewness; if the tail extends towards the smaller values of the variate, the distribution is called negatively skew." (Michael J Moroney,Facts from Figures", 1951)

"Logging size transforms the original skewed distribution into a more symmetrical one by pulling in the long right tail of the distribution toward the mean. The short left tail is, in addition, stretched. The shift toward symmetrical distribution produced by the log transform is not, of course, merely for convenience. Symmetrical distributions, especially those that resemble the normal distribution, fulfill statistical assumptions that form the basis of statistical significance testing in the regression model." (Edward R Tufte,Data Analysis for Politics and Policy", 1974)

"Equal variability is not always achieved in plots. For instance, if the theoretical distribution for a probability plot has a density that drops off gradually to zero in the tails" (as the normal density does), then the variability of the data in the tails of the probability plot is greater than in the center. Another example is provided by the histogram. Since the height of any one bar has a binomial distribution, the standard deviation of the height is approximately proportional to the square root of the expected height; hence, the variability of the longer bars is greater." (John M Chambers et al,Graphical Methods for Data Analysis", 1983)

"If the sample is not representative of the population because the sample is small or biased, not selected at random, or its constituents are not independent of one another, then the bootstrap will fail. […] For a given size sample, bootstrap estimates of percentiles in the tails will always be less accurate than estimates of more centrally located percentiles. Similarly, bootstrap interval estimates for the variance of a distribution will always be less accurate than estimates of central location such as the mean or median because the variance depends strongly upon extreme values in the population." (Phillip I Good & James W Hardin,Common Errors in Statistics" (and How to Avoid Them)", 2003)

"Bell curves don't differ that much in their bells. They differ in their tails. The tails describe how frequently rare events occur. They describe whether rare events really are so rare. This leads to the saying that the devil is in the tails." (Bart Kosko,Noise", 2006)

"Readability in visualization helps people interpret data and make conclusions about what the data has to say. Embed charts in reports or surround them with text, and you can explain results in detail. However, take a visualization out of a report or disconnect it from text that provides context" (as is common when people share graphics online), and the data might lose its meaning; or worse, others might misinterpret what you tried to show." (Nathan Yau,Data Points: Visualization That Means Something", 2013)

"A very different - and very incorrect - argument is that successes must be balanced by failures (and failures by successes) so that things average out. Every coin flip that lands heads makes tails more likely. Every red at roulette makes black more likely. […] These beliefs are all incorrect. Good luck will certainly not continue indefinitely, but do not assume that good luck makes bad luck more likely, or vice versa." (Gary Smith,Standard Deviations", 2014)

"The more complex the system, the more variable (risky) the outcomes. The profound implications of this essential feature of reality still elude us in all the practical disciplines. Sometimes variance averages out, but more often fat-tail events beget more fat-tail events because of interdependencies. If there are multiple projects running, outlier (fat-tail) events may also be positively correlated - one IT project falling behind will stretch resources and increase the likelihood that others will be compromised." (Paul Gibbons,The Science of Successful Organizational Change",  2015)

"Many statistical procedures perform more effectively on data that are normally distributed, or at least are symmetric and not excessively kurtotic" (fat-tailed), and where the mean and variance are approximately constant. Observed time series frequently require some form of transformation before they exhibit these distributional properties, for in their 'raw' form they are often asymmetric." (Terence C Mills,Applied Time Series Analysis: A practical guide to modeling and forecasting", 2019)

"Mean-averages can be highly misleading when the raw data do not form a symmetric pattern around a central value but instead are skewed towards one side [...], typically with a large group of standard cases but with a tail of a few either very high" (for example, income) or low" (for example, legs) values." (David Spiegelhalter,The Art of Statistics: Learning from Data", 2019)

"[…] it is not merely that events in the tails of the distributions matter, happen, play a large role, etc. The point is that these events play the major role and their probabilities are not" (easily) computable, not reliable for any effective use. The implication is that Black Swans do not necessarily come from fat tails; the problem can result from an incomplete assessment of tail events." (Nassim N Taleb,Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"[…] whenever people make decisions after being supplied with the standard deviation number, they act as if it were the expected mean deviation." (Nassim N Taleb,Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"Behavioral finance so far makes conclusions from statics not dynamics, hence misses the picture. It applies trade-offs out of context and develops the consensus that people irrationally overestimate tail risk" (hence need to be 'nudged' into taking more of these exposures). But the catastrophic event is an absorbing barrier. No risky exposure can be analyzed in isolation: risks accumulate. If we ride a motorcycle, smoke, fly our own propeller plane, and join the mafia, these risks add up to a near-certain premature death. Tail risks are not a renewable resource." (Nassim N Taleb,Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"But note that any heavy tailed process, even a power law, can be described in sample" (that is finite number of observations necessarily discretized) by a simple Gaussian process with changing variance, a regime switching process, or a combination of Gaussian plus a series of variable jumps" (though not one where jumps are of equal size […])." (Nassim N Taleb,Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"Once we know something is fat-tailed, we can use heuristics to see how an exposure there reacts to random events: how much is a given unit harmed by them. It is vastly more effective to focus on being insulated from the harm of random events than try to figure them out in the required details" (as we saw the inferential errors under thick tails are huge). So it is more solid, much wiser, more ethical, and more effective to focus on detection heuristics and policies rather than fabricate statistical properties." (Nassim N Taleb,Statistical Consequences of Fat Tails: Real World Preasymptotics, Epistemology, and Applications" 2nd Ed., 2022)

"No one sees further into a generalization than his own knowledge of detail extends." (William James)

"Remember that a p-value merely indicates the probability of a particular set of data being generated by the null model–it has little to say about the size of a deviation from that model" (especially in the tails of the distribution, where large changes in effect size cause only small changes in p-values)." (Clay Helberg)

🔭Data Science: Skewness (Just the Quotes)

"Some distributions [...] are symmetrical about their central value. Other distributions have marked asymmetry and are said to be skew. Skew distributions are divided into two types. If the 'tail' of the distribution reaches out into the larger values of the variate, the distribution is said to show positive skewness; if the tail extends towards the smaller values of the variate, the distribution is called negatively skew." (Michael J Moroney, "Facts from Figures", 1951)

"Logging size transforms the original skewed distribution into a more symmetrical one by pulling in the long right tail of the distribution toward the mean. The short left tail is, in addition, stretched. The shift toward symmetrical distribution produced by the log transform is not, of course, merely for convenience. Symmetrical distributions, especially those that resemble the normal distribution, fulfill statistical assumptions that form the basis of statistical significance testing in the regression model." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"Logging skewed variables also helps to reveal the patterns in the data. […] the rescaling of the variables by taking logarithms reduces the nonlinearity in the relationship and removes much of the clutter resulting from the skewed distributions on both variables; in short, the transformation helps clarify the relationship between the two variables. It also […] leads to a theoretically meaningful regression coefficient." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The logarithmic transformation serves several purposes: (1) The resulting regression coefficients sometimes have a more useful theoretical interpretation compared to a regression based on unlogged variables. (2) Badly skewed distributions - in which many of the observations are clustered together combined with a few outlying values on the scale of measurement - are transformed by taking the logarithm of the measurements so that the clustered values are spread out and the large values pulled in more toward the middle of the distribution. (3) Some of the assumptions underlying the regression model and the associated significance tests are better met when the logarithm of the measured variables is taken." (Edward R Tufte, "Data Analysis for Politics and Policy", 1974)

"The logarithm is an extremely powerful and useful tool for graphical data presentation. One reason is that logarithms turn ratios into differences, and for many sets of data, it is natural to think in terms of ratios. […] Another reason for the power of logarithms is resolution. Data that are amounts or counts are often very skewed to the right; on graphs of such data, there are a few large values that take up most of the scale and the majority of the points are squashed into a small region of the scale with no resolution." (William S. Cleveland, "Graphical Methods for Data Presentation: Full Scale Breaks, Dot Charts, and Multibased Logging", The American Statistician Vol. 38 (4) 1984)

"It is common for positive data to be skewed to the right: some values bunch together at the low end of the scale and others trail off to the high end with increasing gaps between the values as they get higher. Such data can cause severe resolution problems on graphs, and the common remedy is to take logarithms. Indeed, it is the frequent success of this remedy that partly accounts for the large use of logarithms in graphical data display." (William S Cleveland, "The Elements of Graphing Data", 1985)

"If a distribution were perfectly symmetrical, all symmetry-plot points would be on the diagonal line. Off-line points indicate asymmetry. Points fall above the line when distance above the median is greater than corresponding distance below the median. A consistent run of above-the-line points indicates positive skew; a run of below-the-line points indicates negative skew." (Lawrence C Hamilton, "Regression with Graphics: A second course in applied statistics", 1991)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Data that are skewed toward large values occur commonly. Any set of positive measurements is a candidate. Nature just works like that. In fact, if data consisting of positive numbers range over several powers of ten, it is almost a guarantee that they will be skewed. Skewness creates many problems. There are visualization problems. A large fraction of the data are squashed into small regions of graphs, and visual assessment of the data degrades. There are characterization problems. Skewed distributions tend to be more complicated than symmetric ones; for example, there is no unique notion of location and the median and mean measure different aspects of the distribution. There are problems in carrying out probabilistic methods. The distribution of skewed data is not well approximated by the normal, so the many probabilistic methods based on an assumption of a normal distribution cannot be applied." (William S Cleveland, "Visualizing Data", 1993)

"The logarithm is one of many transformations that we can apply to univariate measurements. The square root is another. Transformation is a critical tool for visualization or for any other mode of data analysis because it can substantially simplify the structure of a set of data. For example, transformation can remove skewness toward large values, and it can remove monotone increasing spread. And often, it is the logarithm that achieves this removal." (William S Cleveland, "Visualizing Data", 1993)

"When the distributions of two or more groups of univariate data are skewed, it is common to have the spread increase monotonically with location. This behavior is monotone spread. Strictly speaking, monotone spread includes the case where the spread decreases monotonically with location, but such a decrease is much less common for raw data. Monotone spread, as with skewness, adds to the difficulty of data analysis. For example, it means that we cannot fit just location estimates to produce homogeneous residuals; we must fit spread estimates as well. Furthermore, the distributions cannot be compared by a number of standard methods of probabilistic inference that are based on an assumption of equal spreads; the standard t-test is one example. Fortunately, remedies for skewness can cure monotone spread as well." (William S Cleveland, "Visualizing Data", 1993)

"Use a logarithmic scale when it is important to understand percent change or multiplicative factors. […] Showing data on a logarithmic scale can cure skewness toward large values." (Naomi B Robbins, "Creating More effective Graphs", 2005)

"Distributional shape is an important attribute of data, regardless of whether scores are analyzed descriptively or inferentially. Because the degree of skewness can be summarized by means of a single number, and because computers have no difficulty providing such measures (or estimates) of skewness, those who prepare research reports should include a numerical index of skewness every time they provide measures of central tendency and variability." (Schuyler W Huck, "Statistical Misconceptions", 2008)

"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)

"[The normality] assumption is the least important one for the reliability of the statistical procedures under discussion. Violations of the normality assumption can be divided into two general forms: Distributions that have heavier tails than the normal and distributions that are skewed rather than symmetric. If data is skewed, the formulas we are discussing are still valid as long as the sample size is sufficiently large. Although the guidance about 'how skewed' and 'how large a sample' can be quite vague, since the greater the skew, the larger the required sample size. For the data commonly used in time series and for the sample sizes (which are generally quite large) used, skew is not a problem. On the other hand, heavy tails can be very problematic." (DeWayne R Derryberry, "Basic Data Analysis for Time Series with R" 1st Ed, 2014)

"In statistical theory, location and variability are referred to as the first and second moments of a distribution. The third and fourth moments are called skewness and kurtosis. Skewness refers to whether the data is skewed to larger or smaller values and kurtosis indicates the propensity of the data to have extreme values. Generally, metrics are not used to measure skewness and kurtosis; instead, these are discovered through visual displays [...]" (Peter C Bruce & Andrew G Bruce, "Statistics for Data Scientists: 50 Essential Concepts", 2016)

"A histogram represents the frequency distribution of the data. Histograms are similar to bar charts but group numbers into ranges. Also, a histogram lets you show the frequency distribution of continuous data. This helps in analyzing the distribution (for example, normal or Gaussian), any outliers present in the data, and skewness." (Umesh R Hodeghatta & Umesha Nayak, "Business Analytics Using R: A Practical Approach", 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)

"Mean-averages can be highly misleading when the raw data do not form a symmetric pattern around a central value but instead are skewed towards one side [...], typically with a large group of standard cases but with a tail of a few either very high (for example, income) or low (for example, legs) values." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"With skewed data, quantiles will reflect the skew, while adding standard deviations assumes symmetry in the distribution and can be misleading." (Robert Grant, "Data Visualization: Charts, Maps and Interactive Graphics", 2019)

"Adjusting scale is an important practice in data visualization. While the log transform is versatile, it doesn’t handle all situations where skew or curvature occurs. For example, at times the values are all roughly the same order of magnitude and the log transformation has little impact. Another transformation to consider is the square root transformation, which is often useful for count data." (Sam Lau et al, "Learning Data Science: Data Wrangling, Exploration, Visualization, and Modeling with Python", 2023)

🧩IT: Short Quotes Used in Various Posts

Short quotes used in the various posts:

"A problem well stated is a problem half solved." (Charles F Kettering): Approaching a Query

"An army of principles can penetrate where an army of soldiers cannot." (Thomas Paine): Guiding Principles

"Architecture starts when you carefully put two bricks together." (Ludwig Mies van der Rohe): Guiding Principles

"Data quality requires certain level of sophistication within a company to even understand that it’s a problem." (Colleen Graham): [Who Messed with My Data?]

"Errors, like straws, upon the surface flow;
He who would search for pearls must dive below." (John Dryden): [Who Messed with My Data?]

"Everything should be made as simple as possible, but not simpler." (Albert Einstein): Facts, Principles and Practices

"For every complex problem there is an answer that is clear, simple, and wrong." (Henry L Mencken):  [Who Messed with My Data?]

"I suppose it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail." (Abraham Maslow): [Who Messed with My Data?]

"In preparing for battle I have always found that plans are useless, but planning is indispensable." (Eisenhower quoted by Nixon): Planning Correctly Misunderstood

"It's a bad plan that admits of no modification." (Publilius Syrus): Planning Correctly Misunderstood

"Keep it simple, stupid" (aka KISS): Guiding Principles, Facts, Principles and Practices, Simple, but not that Simple

"Management is doing things right […]" (Peter Drucker): Guiding Principles

"No plan ever survived contact with the enemy." (Carl von Clausewitz): Planning Correctly Misunderstood

"Obey the principles without being bound by them." (Bruce Lee): Guiding Principles

"Students are often able to use algorithms to solve numerical problems without completely understanding the underlying scientific concept." (Eric Mazur): [Who Messed with My Data?]

"The ability to simplify means to eliminate the unnecessary so that the necessary may speak." (Hans Hofmann): Facts, Principles and Practices

"The enemy of a good plan is the dream of a perfect plan." (Carl von Clausewitz): Planning Correctly Misunderstood

"The first obligation of Simplicity is that of using the simplest means to secure the fullest effect" (George Lewes, "Style in Literature"): Designing for Simplicity

"The weakest spot in a good defense is designed to fail." (Mark Lawrence): [Who Messed with My Data?]

"To err is human; to try to prevent recurrence of error is science." (Anon): [Who Messed with My Data?

🧩IT: The Annotated Laws that Govern IT Professionals' Lives - Part I

"A bad idea executed to perfection is still a bad idea." (Norman R Augustine) [Augustine's Law]

"Bad code executed by powerful machines is still bad code." [sql-troubles]

"A great many problems do not have accurate answers, but do have approximate answers, from which sensible decisions can be made." (Berkeley's Law)

"It's easier to take/sell approximations as accurate answers than to find accurate answers. In time people will see no difference in between." [sql-troubles]

"About the time you finish doing something, you know enough to start." (James C Kinser) [Kinser's Law]

"By the time you finish something, the problem changed." [sql-troubles]

"People will more likely repeat their known mistakes than trying something new." [sql-troubles]

"The ofter a method failed, the higher the chances for it to succeed when used by somebody else." [sql-troubles]

"People tend to reuse a method that previously failed (multiple times) than try something new." [sql-troubles]

"By the time we start something, somebody else solved already the problem." [sql-troubles]

"Adding manpower to a late software project makes it later." (Fred P Brooks, "The Mythical Man-Month: Essays", 1975) [Brook's Law]

"Adding manpower seldom solves a problem that requires intelligent effort." [sql-troubles]

"The easiest way to make a project on time is to to move the deadline as suited." [sql-troubles]

"An object will fall so as to do the most damage." [Law of selective gravity]

"A bug will appear to do the most damage." [sql-troubles]

"Anything can be made to work if you fiddle with it long enough." (Wyszkowski's second law)

"Some problems do require infinite time." [sql-troubles]

"Build a system that even a fool can use, and only a fool will want to use it." [Shaw's principle]

"Doing it the hard way is always easier." (Murphy's paradox)

"Doing it the easy way is always harder." [sql-troubles]

"Don't force it - get a bigger hammer." [Anthony's law of force]

"Don't optimize it, get a more powerful machine." [sql-troubles]

"Every solution breeds new problems." [Murphy's laws]

"Every new problem multiplies the possible solutions." [sql-troubles]

"It's easier to change the problem to fit the solution." [sql-troubles]

"Everyone has a scheme that will not work." [Howe's law]

"Any scheme can work by accident." [sql-troubles]

"It takes more than an accident for a scheme to work." [sql-troubles]

"Everything goes wrong all at once." (Quantized revision of Murphy's law)

"Small events converge toward bigger events." [sql-troubles]

"Things already went wrong before we observe them as such." [sql-troubles]

"If a problem causes many meetings, the meetings eventually become more important than the problem." (Arthur Bloch, "Murphy's Law (Price/Stern/Sloan", 1977) (Hendrickson’s Law)

"More meetings tend to create more problems." [sql-troubles]

 "Fewer meetings tend to create more problems." [sql-troubles]

"If a project is not worth doing at all, it's not worth doing well." (Gordon's first law)

"The more a project is not worth doing, the more attention will attract."  [sql-troubles]

"If an experiment works, something has gone wrong." [Finagle's first law]

"If anything can go wrong, it will." [Murphy's laws]

"Things go wrong at a faster pace than one can find solutions." [sql-troubles]

"If there are two or more ways to do something, and one of those ways can result in a catastrophe, then someone will do it." [Murphy's Laws]

"It's enough one way, for things to result in catastrophes." [sql-troubles]

"Sometimes it's better to do nothing than make things worse." [sql-troubles]

"Once all the known wrong solutions were exhausted, one discovers a new wrong solution." [sql-troubles]

"If they know nothing of what you are doing, they suspect you are doing nothing." (Robert J Graham et al, "The Complete Idiot's Guide to Project Management", 2007)  [Graham's Law]

"People are good at ignoring the obvious." [sql-troubles]

"The more one explains, the more one is misunderstood." [sql-troubles] 

"If you mess with a thing long enough, it'll break." [Schmidt's law]

"Things break by design." [sql-troubles]

"One can learn to break things, by simply playing with them." [sql-troubles] 

"It's easier to break than design things. One can find thousands ways on how to break the same thing." [sql-troubles] 

"In any collection of data, the figure most obviously correct, beyond all need of checking, is the mistake." (Finagle's third law)

"In any collection of data there's at least a mistake." [sql-troubles]

"In any given set of circumstances, the proper course of action is determined by subsequent events." [McDonald's corollary to Murphy's laws]

"In crises that force people to choose among alternative courses of action, most people will choose the worst one possible." (Rudin's law)

"People go wrong with confidence." [sql-troubles]

"The more alternatives, the higher the chances to go wrong." [sql-troubles] 

"Information necessitating a change of design will be conveyed to the designer after - and only after - the plans are complete." [First law of revision:]

"In simple cases, presenting one obvious right way versus one obvious wrong way, it is often wiser to choose the wrong way so as to expedite subsequent revision." (First corollary

"The designer will get ahead of the design." [sql-troubles] 

"It is impossible to make anything foolproof because fools are so ingenious." (Murphy's second corollary)

"It works better if you plug it in." (Sattinger's law)

"It works longer if you don't plug it in." [sql-troubles]

"It's not a question of IF the car will break down, but WHEN it will break down." (Murphy's theory of automobiles)

"It's not a question of IF a program will break down, but when the code will break down." [sql-troubles]

"The longer a program runs smoothly, the higher the chances that will break down soon." [sql-troubles]

"Left to themselves, things tend to go from bad to worse." (Murphy's first corollary)

"The more on tries to fix things, the faster everything goes worse." [sql-troubles]

"Logic is a systematic method of coming to the wrong conclusion with confidence." (Manly's maxim)

 "One doesn't need logic to arrive at the right conclusion." [sql-troubles]

"Matter will be damaged in direct proportion to its value." (Murphy's constant)

"Most problems have either many answers or no answer. Only a few problems have a single answer." [Berkeley's Law]

"It's better to have a multitude of approximate solutions than one correct solution." [sql-troubles]

"Negative expectations yield negative results. Positive expectations yield negative results." (Non-reciprocal law of expectations)

"Negative results yield when there are no expectations." [sql-troubles]

"No matter how many things have gone wrong, there remains at least one more thing that will go wrong." (Murphy's law of the infinite)

"Things can go wrong in a multitude of ways." [sql-troubles]

"No matter how minor the job is, it's still over $50." (Murphy's rule of auto repair)

"No matter what the experiment's result, there will always be someone eager to: (i) misinterpret it, (ii) fake it, or (c) believe it supports his own pet theory." (Finagle's second law)

"It's easier to fake the experiment to get the right results." [sql-troubles]

"Nothing ever goes away." (Commoner's second law of ecology)

"Things do go away, but tend to come back." [sql-troubles]

"Nothing is as easy as it looks." (Murphy's first corollary)

"All things look simple until one dives deeper." [sql-troubles]

"Nothing is ever so bad that it can't get worse." (Gattuso's extension of Murphy's Law)

"Once a job is fouled up, anything done to improve it only makes it worse." (Finagle's fourth law)

"Once a mistake is corrected, a second mistake will become apparent." (Murphy's law of revision)

"Correcting mistakes introduces other mistakes." [sql-troubles]

"The chief cause of problems is solutions." [Sevareid's Law]

"The more time you spend in reporting on what you are doing, the less time you have to do anything. Stability is achieved when you spend all your time doing nothing but reporting on the nothing you are doing." [Cohn's Law]

"Reporting increases the needs for more information. The less one reports, the lower the need for further information." [sql-troubles]

"The more innocuous the modification appears to be, the further its influence will extend and the more plans will have to be redrawn." [H B Fyfe's second law of revision]

"The only thing more costly than stretching the schedule of an established development program is accelerating it, which is itself the most costly action known to man." (Norman R Augustine, "Augustine's Laws", 1983) [Law of economic unipolarity]

"The other line moves faster." (Etorre's observation)

"The other team moves faster." [sql-troubles]

"If you change lines, the one you just left will start to move faster than the one you are now in." (O'Brien's variation

"If you change a line, the whole codes breaks." [sql-troubles]

"The longer you wait in line, the greater the likelihood that you are in the wrong line." (The Queue Principal)

"The longer you wait for a deliverable, the greater the likelihood that it contains bugs." [sql-troubles]

"The perceived usefulness of an article is inversely proportional to its actual usefulness once bought and paid for." (Glatum's law of materialistic acquisitiveness)

"The probability of anything happening is in inverse ratio to its desirability." (Gumperson's law)

"The solution to a problem changes the problem." [Peers's Law]

"A problem to a solution changes thr solution." [sql-troubles]

"The tasks to do immediately are the minor ones; otherwise, you’ll forget them. The major ones are often better to defer. They usually need more time for reflection. Besides, if you forget them, they’ll remind you." [Wolf ’s Law of Management]

"There are two states to any large project: Too early to tell and too late to stop." (Ernest Fitzgerald) [Fitzgerald's Law]

"There is a solution to every problem; the only difficulty is finding it." [Evvie Nef's Law]

"There is a solution to every problem we are not trying to solve." [sql-troubles]

"Finding problems is easier than finding solutions." [sql-troubles]

"One stumbles upon the same problen twice." [sql-troubles]

"There is no mechanical problem so difficult that it cannot be solved by brute strength and ignorance. [William's Law]

"There's no software problem so difficult that can't be solved by brute force and ignorance." [sql-troubles]

"There's always one more bug." (Lubarsky's law of cybernetic entomology)

"Software solutions diverge to a set of bugs." [sql-troubles

"Things get worse under pressure." [Murphy's law of thermodynamics]

"Things get worse also without pressure." [sql-troubles]

"Things go right gradually, but things go wrong all at once." (Murphy's asymmetry principle)

"Tolerances will accumulate unidirectionally toward maximum difficulty of assembly. (Klipstein's law)

"Two wrongs are only the beginning." (Kohn's corollary to Murphy's law)

"One wrong can be the beginning of another." [sql-troubles]

"When all else fails, read the instructions." [Cahn's axiom]

"Even if you read the instructions, things fall." [sql-troubles]

"When an error has been detected and corrected, it will be found to have been correct in the first place." [Scott's second law]

"Any two related problems may look the same when regarded from same perspective." [sql-troubles]

"When in doubt, use a bigger hammer." [Dobbins’ Law]

"When taking something apart to fix a minor malfunction, you will cause a major malfunction." (Murphy's second law of construction)

"Whenever you set out to do something, something else must be done first." (Murphy's sixth corollary)

"While the difficulties and dangers of problems tend to increase at a geometric rate, the knowledge and manpower qualified to deal with these problems tend to increase linearly." [Dror's First Law]

"Beyond a point, the problems are so complex that people can't differentiate between geometric and linear rates." [sql-troubles]

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🧩IT: Idioms, Sayings, Proverbs and Other Words of Wisdom

In IT setups one can hear many idioms, sayings and other type of words of wisdom that make the audience smile, even if some words seem to rub salt in the wounds. These are some of the idioms met in IT meetings or literature. Frankly, it's worth to write more about each of them, and this it the purpose of the "project". 

"A bad excuse is better than none"

"A bird in the hand is worth two in the bush": a working solution is worth more than hypothetically better solutions. 

"A drowning man will clutch at a straw": a drowning organization will clutch to the latest hope

"A friend in need (is a friend indeed)": 

"A journey of a thousand miles begins with a single step"

"A little learning is a dangerous thing"

"A nail keeps a shoe, a shoe a horse, a horse a man, a man a castle" (cca 1610): A nail keeps the shoe

"A picture is worth a thousand words"

"A stitch in time (saves nine)"

"Actions speak louder than words"

"All good things must come to an end"

"All generalizations are false" [attributed to Mark Twain, Alexandre Dumas (Père)]: Cutting though Complexity

"All the world's a stage, And all [...] merely players": A look forward

"All roads lead to Rome"

"All is well that ends well"

"An ounce of prevention is worth a pound of cure"

"Another day, another dollar"

"As you sow so shall you reap"

"Beauty is in the eye of the beholder"

"Better late than never": SQL Server and Excel Data

"Better safe than sorry": Deleting obsolete companies

"Big fish eat little fish"

"Better the Devil you know (than the Devil you do not)": 

"Calm seas never made a good sailor"

"Count your blessings"

"Dead men tell no tales"

"Do not bite the hand that feeds you"

"Do not change horses in midstream"

"Do not count your chickens before they are hatched"

"Do not cross the bridge till you come to it"

"Do not judge a book by its cover"

"Do not meet troubles half-way"

"Do not put all your eggs in one basket"

"Do not put the cart before the horse"

"Do not try to rush things; ignore matters of minor advantage" (Confucius): A tale of two cities II

"Do not try to walk before you can crawl"

"Doubt is the beginning, not the end, of wisdom"

"Easier said than done"

"Every cloud has a silver lining"

"Every little bit helps"

"Every picture tells a story"

"Failing to plan is planning to fail": Planning correctly misunderstood...

"Faith will move mountains"

"Fake it till you make it"

"Fight fire with fire"

"First impressions are the most lasting"

"First things first": Ways of looking at data

"Fish always rots from the head downwards"

"Fools rush in (where angels fear to tread)" (Alexander Pope, "An Essay on Criticism", cca. 1711): A tale of two cities II

"Half a loaf is better than no bread"

"Haste makes waste"

"History repeats itself"

"Hope for the best, and prepare for the worst"

"If anything can go wrong, it will" (Murphy's law)

"If it ain't broke, don't fix it.": Approaching a query

"If you play with fire, you will get burned"

"If you want a thing done well, do it yourself"

"Ignorance is bliss"

"Imitation is the sincerest form of flattery"

"It ain't over till/until it's over"

"It is a small world"

"It is better to light a candle than curse the darkness"

"It is never too late": A look back, All-knowing developers are back...

"It's a bad plan that admits of no modification." (Publilius Syrus): Planning Correctly Misunderstood I

"It’s not an adventure until something goes wrong." (Yvon Chouinard): Documentation - Lessons learned

"It is not enough to learn how to ride, you must also learn how to fall"

"It takes a whole village to raise a child"

"It will come back and haunt you"

"Judge not, that ye be not judged"

"Kill two birds with one stone"

"Knowledge is power, guard it well"

"Learn a language, and you will avoid a war" (Arab proverb)

"Less is more"

"Life is what you make it"

"Many hands make light work"

"Moderation in all things"

"Money talks"

"More haste, less speed"

"Necessity is the mother of invention"

"Never judge a book by its cover"

"Never say never"

"Never too old to learn"

"No man can serve two masters"

"No pain, no gain"

"No plan ever survived contact with the enemy.' (Carl von Clausewitz): Planning Correctly Misunderstood I

"Oil and water do not mix"

"One-man show": series

"One man's trash is another man's treasure"

"One swallow does not make a summer"

"Only time will tell": The Software Quality Perspective and AI, Microsoft Fabric, It’s all about Partnership II, Access vs. LightSwitch

"Patience is a virtue"

"Poke the bear": Mea Culpa - A Look Forward

"Practice makes perfect"

"Practice what you preach"

"Prevention is better than cure"

"Rules were made to be broken"

"Seek and ye shall find"

"Some are more equal than others" (George Orwell, "Animal Farm")

"Spoken words fly away, written words remain." ["Verba volant, scripta manent"]: Documentation - Lessons learned

"Strike while the iron is hot"

"Technology is dead": Dashboards Are Dead & Other Crap, programming is dead

"The best defense is a good offense"

"The bets are off":  A look forward

"The bigger they are, the harder they fall"

"The devil is in the detail": Copilot Stories Part IV, Cutting through Complexity, More on SQL Databases, The Analytics Marathon, The Choice of Tools in PM, Who Messed with My Data?, 

"The die is cast"

"The exception which proves the rule"

"The longest journey starts with a single step"

"The pursuit of perfection is a fool's errand"

"There are two sides to every question"

"There is no smoke without fire"

"There's more than one way to skin a cat" (cca. 1600s)

"There is no I in team"

"There is safety in numbers"

"Those who do not learn from history are doomed to repeat it" (George Santayana)

"Time is money"

"To learn a language is to have one more window from which to look at the world" (Chinese proverb)[5

"Too little, too late"

"Too much of a good thing"

"Truth is stranger than fiction"

"Two birds with one stone": Deleting sequential data...

"Two heads are better than one": Pair programming

"Two wrongs (do not) make a right"

"United we stand, divided we fall"

"Use it or lose it"

"Unity is strength"

"Variety is the spice of life." (William Cowper)

"Virtue is its own reward"

"Well begun is half done"

"What does not kill me makes me stronger"

"Well done is better than well said"

"What cannot be cured must be endured"

"What goes around, comes around"

"When life gives you lemons, make lemonade"

"When the cat is away, the mice will play"

"When the going gets tough, the tough get going"

"Where there is a will there is a way"

"With great power comes great responsibility"

"Work expands so as to fill the time available"

"You are never too old to learn": All-Knowing Developers are Back in Demand?

"You can lead a horse to water, but you cannot make it drink"

"You cannot make an omelet without breaking eggs"

"(You cannot) teach an old dog new tricks"

"You must believe and not doubt at all": Believe and not doubt

"Zeal without knowledge is fire without light"

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References:
[1] Wikipedia (2024) List of proverbial phrases [link]

🕸Systems Engineering: A Play of Problems (Much Ado about Nothing)

Disclaimer: This post was created just for fun. No problem was hurt or solved in the process! 
Updated: 12-Jun-2024

On Problems

Everybody has at least a problem. If somebody doesn’t have a problem, he’ll make one. If somebody can't make a problem, he can always find a problem. One doesn't need to search long for finding a problem. Looking for a problem one sees more problems. 

Not having a problem can easily become a problem. It’s better to have a problem than none. The none problem is undefinable, which makes it a problem. 

Avoiding a problem might lead you to another problem. Some problems are so old, that's easier to ignore them. 

In every big problem there’s a small problem trying to come out. Most problems can be reduced to smaller problems. A small problem may hide a bigger problem. 

It’s better to solve a problem when is still small, however problems can be perceived only when they grow bigger (big enough). 

In the neighborhood of a problem there’s another problem getting closer. Problems tend to attract each other. 

Between two problems there’s enough place for a third to appear. The shortest path between two problems is another problem. 

Two problems that appear together in successive situations might be the parts of the same problem. 

A problem is more than the sum of its parts.

Any problem can be simplified to the degree that it becomes another problem. 

The complementary of a problem is another problem. At the intersection/reunion of two problems lies another problem.

The inverse of a problem is another problem more complex than the initial problem.

Defining a problem correctly is another problem. A known problem doesn’t make one problem less. 

When a problem seems to be enough, a second appears. A problem never comes alone.  The interplay of the two problems creates a third.

Sharing the problems with somebody else just multiplies the number of problems. 

Problems multiply beyond necessity. Problems multiply beyond our expectations. Problems multiply faster than we can solve them. 

Having more than one problem is for many already too much. Between many big problems and an infinity of problems there seem to be no big difference. 

Many small problems can converge toward a bigger problem. Many small problems can also diverge toward two bigger problems. 

When neighboring problems exist, people tend to isolate them. Isolated problems tend to find other ways to surprise.

Several problems aggregate and create bigger problems that tend to suck within the neighboring problems.

If one waits long enough some problems will solve themselves or it will get bigger. Bigger problems exceed one's area of responsibility. 

One can get credit for a self-created problem. It takes only a good problem to become famous.

A good problem can provide a lifetime. A good problem has the tendency to kick back where it hurts the most. One can fall in love with a good problem. 

One should not theorize before one has a (good) problem. A problem can lead to a new theory, while a theory brings with it many more problems. 

If the only tool you have is a hammer, every problem will look like a nail. (paraphrasing Abraham H Maslow)

Any field of knowledge can be covered by a set of problems. A field of knowledge should be learned by the problems it poses.

A problem thoroughly understood is always fairly simple, but unfairly complex. (paraphrasing Charles F Kettering)

The problem solver created usually the problem. 

Problem Solving

Break a problem in two to solve it easier. Finding how to break a problem is already another problem. Deconstructing a problem to its parts is no guarantee for solving the problem.

Every problem has at least two solutions from which at least one is wrong. It’s easier to solve the wrong problem. 

It’s easier to solve a problem if one knows the solution already. Knowing a solution is not a guarantee for solving the problem.

Sometimes a problem disappears faster than one can find a solution. 

If a problem has two solutions, more likely a third solution exists. 

Solutions can be used to generate problems. The design of a problem seldom lies in its solutions. 

The solution of a problem can create at least one more problem. 

One can solve only one problem at a time. 

Unsolvable problems lead to problematic approximations. There's always a better approximation, one just needs to find it. One needs to be o know when to stop searching for an approximation. 

There's not only a single way for solving a problem. Finding another way for solving a problem provides more insight into the problem. More insight complicates the problem unnecessarily. 

Solving a problem is a matter of perspective. Finding the right perspective is another problem.

Solving a problem is a matter of tools. Searching for the right tool can be a laborious process. 

Solving a problem requires a higher level of consciousness than the level that created it. (see Einstein) With the increase complexity of the problems one an run out of consciousness.

Trying to solve an old problem creates resistance against its solution(s). 

The premature optimization of a problem is the root of all evil. (paraphrasing Donald Knuth)

A great discovery solves a great problem but creates a few others on its way. (paraphrasing George Polya)

Solving the symptoms of a problem can prove more difficult that solving the problem itself.

A master is a person who knows the solutions to his problems. To learn the solutions to others' problems he needs a pupil. 

"The final test of a theory is its capacity to solve the problems which originated it." (George Dantzig) It's easier to theorize if one has a set of problems.

A problem is defined as a gap between where you are and where you want to be, though nobody knows exactly where he is or wants to be.

Complex problems are the problems that persist - so are minor ones.

"The problems are solved, not by giving new information, but by arranging what we have known since long." (Ludwig Wittgenstein, 1953) Some people are just lost in rearranging. 

Solving problems is a practical skill, but impractical endeavor. (paraphrasing George Polya) 

"To ask the right question is harder than to answer it." (Georg Cantor) So most people avoid asking the right question.

Solve more problems than you create.

They Said It

"A great many problems do not have accurate answers, but do have approximate answers, from which sensible decisions can be made." (Berkeley's Law)

"A problem is an opportunity to grow, creating more problems. [...] most important problems cannot be solved; they must be outgrown." (Wayne Dyer)

"A system represents someone's solution to a problem. The system doesn't solve the problem." (John Gall, 1975)

"As long as a branch of science offers an abundance of problems, so long is it alive." (David Hilbert)

"Complex problems have simple, easy to understand, wrong answers." [Grossman's Misquote]

"Every solution breeds new problems." [Murphy's laws]

"Given any problem containing n equations, there will be n+1 unknowns." [Snafu]

"I have not seen any problem, however complicated, which, when you looked at it in the right way, did not become still more complicated." (Paul Anderson)

"If a problem causes many meetings, the meetings eventually become more important than the problem." (Hendrickson’s Law)

"If you think the problem is bad now, just wait until we’ve solved it." (Arthur Kasspe) [Epstein’s Law]

"Inventing is easy for staff outfits. Stating a problem is much harder. Instead of stating problems, people like to pass out half- accurate statements together with half-available solutions which they can't finish and which they want you to finish." [Katz's Maxims]

"It is better to do the right problem the wrong way than to do the wrong problem the right way." (Richard Hamming)

"Most problems have either many answers or no answer. Only a few problems have a single answer." [Berkeley's Law]

"Problems worthy of attack prove their worth by fighting back." (Piet Hein)

Rule of Accuracy: "When working toward the solution of a problem, it always helps if you know the answer."
Corollary: "Provided, of course, that you know there is a problem."

"Some problems are just too complicated for rational logical solutions. They admit of insights, not answers." (Jerome B Wiesner, 1963)

"Sometimes, where a complex problem can be illuminated by many tools, one can be forgiven for applying the one he knows best." [Screwdriver Syndrome]

"The best way to escape from a problem is to solve it." (Brendan Francis)

"The chief cause of problems is solutions." [Sevareid's Law]

"The first step of problem solving is to understand the existing conditions." (Kaoru Ishikawa)

"The human race never solves any of its problems, it only outlives them." (David Gerrold)

"The most fruitful research grows out of practical problems."  (Ralph B Peck)

"The problem-solving process will always break down at the point at which it is possible to determine who caused the problem." [Fyffe's Axiom]

"The worst thing you can do to a problem is solve it completely." (Daniel Kleitman)

"The easiest way to solve a problem is to deny it exists." (Isaac Asimov)

"The solution to a problem changes the problem." [Peers's Law]

"There is a solution to every problem; the only difficulty is finding it." [Evvie Nef's Law]

"There is no mechanical problem so difficult that it cannot be solved by brute strength and ignorance. [William's Law]

"Today's problems come from yesterday’s 'solutions'." (Peter M Senge, 1990)

"While the difficulties and dangers of problems tend to increase at a geometric rate, the knowledge and manpower qualified to deal with these problems tend to increase linearly." [Dror's First Law]

"You are never sure whether or not a problem is good unless you actually solve it." (Mikhail Gromov)

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More quotes on Problem solving at QuotableMath.blogpost.com

Resources:
Murphy's laws and corollaries (link)

🤝Governance: Responsibility (Just the Quotes)

"Weak character coupled with honored place, meager knowledge with large plans, limited powers with heavy responsibility, will seldom escape disaster." ("I Ching" ["Book of Changes"], cca. 600 BC)

"The only way for a large organization to function is to decentralize, to delegate real authority and responsibility to the man on the job. But be certain you have the right man on the job." (Robert E Wood, 1951)

"[...] authority - the right by which superiors are able to require conformity of subordinates to decisions - is the basis for responsibility and the force that binds organization together. The process of organizing encompasses grouping of activities for purposes of management and specification of authority relationships between superiors and subordinates and horizontally between managers. Consequently, authority and responsibility relationships come into being in all associative undertakings where the superior-subordinate link exists. It is these relationships that create the basic character of the managerial job." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"[...] authority for given tasks is limited to that for which an individual may properly held responsible." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"If charts do not reflect actual organization and if the organization is intended to be as charted, it is the job of effective management to see that actual organization conforms with that desired. Organization charts cannot supplant good organizing, nor can a chart take the place of spelling out authority relationships clearly and completely, of outlining duties of managers and their subordinates, and of defining responsibilities." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"Responsibility cannot be delegated. While a manager may delegate to a subordinate authority to accomplish a service and the subordinate in turn delegate a portion of the authority received, none of these superiors delegates any of his responsibility. Responsibility, being an obligation to perform, is owed to one's superior, and no subordinate reduces his responsibility by assigning the duty to another. Authority may be delegated, but responsibility is created by the subordinate's acceptance of his assignment." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"Viewed internally with respect to the enterprise, responsibility may be defined as the obligation of a subordinate, to whom a superior has assigned a duty, to perform the service required. The essence of responsibility is, then, obligation. It has no meaning except as it is applied to a person." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"You can delegate authority, but you can never delegate responsibility by delegating a task to someone else. If you picked the right man, fine, but if you picked the wrong man, the responsibility is yours - not his." (Richard E Krafve, The Boston Sunday Globe, 1960)

"Modern organization makes demands on the individual to learn something he has never been able to do before: to use organization intelligently, purposefully, deliberately, responsibly [...] to manage organization [...] to make [...] his job in it serve his ends, his values, his desire to achieve." (Peter F Drucker, The Age of Discontinuity, 1968)

"[Management by objectives is] a process whereby the superior and the subordinate managers of an enterprise jointly identify its common goals, define each individual's major areas of responsibility in terms of the results expected of him, and use these measures as guides for operating the unit and assessing the contribution of each of its members." (Robert House, "Administrative Science Quarterly", 1971)

"'Management' means, in the last analysis, the substitution of thought for brawn and muscle, of knowledge for folkways and superstition, and of cooperation for force. It means the substitution of responsibility for obedience to rank, and of authority of performance for authority of rank. (Peter F Drucker, "People and Performance", 1977)

"[...] the first criterion in identifying those people within an organization who have management responsibility is not command over people. It is responsibility for contribution. Function rather than power has to be the distinctive criterion and the organizing principle." (Peter F Drucker, "People and Performance", 1977)

"The productivity of work is not the responsibility of the worker but of the manager." (Peter F Drucker, "Management in Turbulent Times", 1980)

"By assuming sole responsibility for their departments, managers produce the very narrowness and self-interest they deplore in subordinates. When subordinates are relegated to their narrow specialties, they tend to promote their own practical interests, which then forces other subordinates into counter-advocacy. The manager is thereby thrust into the roles of arbitrator, judge, and referee. Not only do priorities become distorted, but decisions become loaded with win/lose dynamics. So, try as the manager might, decisions inevitably lead to disgruntlement and plotting for the next battle." (David L Bradford & Allan R Cohen, "Managing for Excellence", 1984)

"The man who delegates responsibilities for running the company, without knowing the intimate details of what is involved, runs the enormous risk of rendering himself superfluous." (Harold Geneen, "Managing", 1984)

"Leadership is the total effect you have on the people and events around you. This effect is your influence. Effective leading is being consciously responsible for your organizational influence. [...] The essence of leadership is knowing that YOU CAN NEVER NOT LEAD. You lead by acts of commission and acts of omission." (Kenneth Schatz & Linda Schatz, "Managing by Influence", 1986)

"Looking for differences between the more productive and less productive organizations, we found that the most striking difference is the number of people who are involved and feel responsibility for solving problems." (Michael McTague, "Personnel Journal", 1986)

"Management has a responsibility to explain to the employee how the routine job contributes to the business's objectives. If management cannot explain the value of the job, then it should be eliminated and the employee reassigned." (Douglas M Reid, Harvard Business Review, 1986)

"A systematic effort must be made to emphasize the group instead of the individual. [...] Group goals and responsibilities can usually overcome any negative reactions to the individual and enforce a standard of cooperation that is attainable by persuasion or exhortation." (Eugene Raudsepp, MTS Digest, 1987)

"An individual without information cannot take responsibility; an individual who is given information cannot help but take responsibility." (Jan Carlzon, "Moments of Truth", 1987)

"Executives have to start understanding that they have certain legal and ethical responsibilities for information under their control." (Jim Leeke, PC Week, 1987)

"If responsibility - and particularly accountability - is most obviously upwards, moral responsibility also reaches downwards. The commander has a responsibility to those whom he commands. To forget this is to vitiate personal integrity and the ethical validity of the system." (Roger L Shinn, "Military Ethics", 1987)

[...] quality assurance is the job of the managers responsible for the product. A separate group can't 'assure' much if the responsible managers have not done their jobs properly. [...] Managers should be held responsible for quality and not allowed to slough off part of their responsibility to a group whose name sounds right but which cannot be guaranteed quality if the responsible managers have not been able to do so." (Philip W. Metzger, "Managing Programming People", 1987)

"Responsibility is a unique concept [...] You may share it with others, but your portion is not diminished. You may delegate it, but it is still with you. [...] If responsibility is rightfully yours, no evasion, or ignorance or passing the blame can shift the burden to someone else. Unless you can point your finger at the man who is responsible when something goes wrong, then you have never had anyone really responsible." (Hyman G Rickover, "The Rickover Effect", 1992)

"If you treat people as though they are responsible, they tend to behave that way." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"You can’t delegate responsibility without giving a person authority commensurate with it." (James P Lewis, "Project Planning, Scheduling, and Control" 3rd Ed., 2001)

"What do people do today when they don’t understand 'the system'? They try to assign responsibility to someone to fix the problem, to oversee 'the system', to coordinate and control what is happening. It is time we recognized that 'the system' is how we work together. When we don’t work together effectively putting someone in charge by its very nature often makes things worse, rather than better, because no one person can understand 'the system' well enough to be responsible. We need to learn how to improve the way we work together, to improve 'the system' without putting someone in charge, in order to make things work." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"In order to cultivate a culture of accountability, first it is essential to assign it clearly. People ought to clearly know what they are accountable for before they can be held to it. This goes beyond assigning key responsibility areas (KRAs). To be accountable for an outcome, we need authority for making decisions, not just responsibility for execution. It is tempting to refrain from the tricky exercise of explicitly assigning accountability. Executives often hope that their reports will figure it out. Unfortunately, this is easier said than done." (Sriram Narayan, "Agile IT Organization Design: For Digital Transformation and Continuous Delivery", 2015)

"Any software project must have a technical leader, who is responsible for all technical decisions made by the team and have enough authority to make them. Responsibility and authority are two mandatory components that must be present in order to make it possible to call such a person an architect." (Yegor Bugayenko, "Code Ahead", 2018)

"Responsibility means an inevitable punishment for mistakes; authority means full power to make them." (Yegor Bugayenko, "Code Ahead", 2018)