"At the present time there is a total lack of standardization in the form of diagram to use for nearly all classes of representation. This makes it difficult to compare reports of different investigators on the same subject because their diagrams are not constructed alike." (William C Marshall,Graphical methods for schools, colleges, statisticians, engineers and executives", 1921)
"Precision is expressed by an international standard, viz., the standard error. It measures the average of the difference between a complete coverage and a long series of estimates formed from samples drawn from this complete coverage by a particular procedure or drawing, and processed by a particular estimating formula." (W Edwards Deming,On the Presentation of the Results of Sample Surveys as Legal Evidence", Journal of the American Statistical Association Vol 49 (268), 1954)
"The relevant question is not whether ANOVA assumptions are met exactly, but rather whether the plausible violations of the assumptions have serious consequences on the validity of probability statements based on the standard assumptions." (Gene V Glass et al,Consequences of Failure to Meet Assumptions Underlying the Fixed Effects Analyses of Variance and Covariance", Review of Educational Research Vol. 42 (3), 1972)
"Exploratory data analysis, EDA, calls for a relatively free hand in exploring the data, together with dual obligations: (•) to look for all plausible alternatives and oddities - and a few implausible ones, (graphic techniques can be most helpful here) and (•) to remove each appearance that seems large enough to be meaningful - ordinarily by some form of fitting, adjustment, or standardization [...] so that what remains, the residuals, can be examined for further appearances." (John W Tukey,Introduction to Styles of Data Analysis Techniques", 1982)
"The conditions under which many data graphics are produced - the lack of substantive and quantitative skills of the illustrators, dislike of quantitative evidence, and contempt for the intelligence of the audience-guarantee graphic mediocrity. These conditions engender graphics that (1) lie; (2) employ only the simplest designs, often unstandardized time-series based on a small handful of data points; and (3) miss the real news actually in the data." (Edward R Tufte,The Visual Display of Quantitative Information", 1983)
"It would help if the standard statistical programs did not generate t statistics in such profusion. The programs might be written to ask, 'Do you really have a probability sample?', 'By what standard would you judge a fitted coefficient large or small?' Or perhaps they could merely say, printed in bold capitals beside each equation, 'So What Else Is New?'" (Donald M McCloskey,The Loss Function Has Been Mislaid: The Rhetoric of Significance Tests", American Economic Review Vol. 75, 1985)
"When evaluating a model, at least two broad standards are relevant. One is whether the model is consistent with the data. The other is whether the model is consistent with the ‘real world.’" (Kenneth Bollen,Structural Equations with Latent Variable", 1989)
"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 Pattern", 1992)
"One important aspect of reality is improvisation; as a result of special structure in a set of data, or the finding of a visualization method, we stray from the standard methods for the data type to exploit the structure or the finding." (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)
"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 definition of a ‘good model’ is when everything inside it is visible, inspectable and testable. It can be communicated effortlessly to others. A ‘bad model’ is a model that does not meet these standards, where parts are hidden, undefined or concealed and it cannot be inspected or tested; these are often labelled black box models." (Hördur V Haraldsson & Harald U Sverdrup,Finding Simplicity in Complexity in Biogeochemical Modelling" [inEnvironmental Modelling: Finding Simplicity in Complexity", Ed. by John Wainwright and Mark Mulligan, 2004])
"The inevitability of variability complicates the evaluation and use of data. It must be recognized that many uses require data quality that may be difficult to achieve. There are minimum quality standards required for every measurement situation (sometimes called data quality objectives). These standards should be established in advance and both the producer and the user must be able to determine whether they have been met. The only way that this can be accomplished is to attain statistical control of the measurement process and to apply valid statistical procedures in the analysis of the data." (Cheryl Cihon & John K Taylor, "Statistical Techniques for Data Analysis" 2nd. ed., 2005)
"Regularization works because it is the sum of the coefficients of the predictor variables, therefore it’s important that they’re on the same scale or the regularization may find it difficult to converge, and variables with larger absolute coefficient values will greatly influence it, generating an infective regularization. It’s good practice to standardize the predictor values or bind them to a common min‐max, such as the [‐1,+1] range." (Luca Massaron & John P Mueller,Python for Data Science For Dummies", 2015)
"The closer that sample-selection procedures approach the gold standard of random selection - for which the definition is that every individual in the population has an equal chance of appearing in the sample - the more we should trust them. If we don’t know whether a sample is random, any statistical measure we conduct may be biased in some unknown way." (Richard E Nisbett,Mindware: Tools for Smart Thinking", 2015)
"Measurements must be standardized. There must be clear, replicable, and precise procedures for collecting data so that each person who collects it does it in the same way." (Daniel J Levitin,Weaponized Lies", 2017)
"The danger of overfitting is particularly severe when the training data is not a perfect gold standard. Human class annotations are often subjective and inconsistent, leading boosting to amplify the noise at the expense of the signal. The best boosting algorithms will deal with overfitting though regularization. The goal will be to minimize the number of non-zero coefficients, and avoid large coefficients that place too much faith in any one classifier in the ensemble." (Steven S Skiena,The Data Science Design Manual", 2017)
"There is often no one 'best' visualization, because it depends on context, what your audience already knows, how numerate or scientifically trained they are, what formats and conventions are regarded as standard in the particular field you’re working in, the medium you can use, and so on. It’s also partly scientific and partly artistic, so you get to express your own design style in it, which is what makes it so fascinating." (Robert Grant,Data Visualization: Charts, Maps and Interactive Graphics", 2019)
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