"Many people imagine that graphic charts cannot be understood except by expert mathematicians who have devoted years of study to the subject. This is a mistaken idea, and if instead of passing over charts as if they were something beyond their comprehension more people would make an effort to read them, much valuable time would be saved. It is true that some charts covering technical data are difficult even for an expert mathematician to understand, but this is more often the fault of the person preparing the charts than of the system." (Allan C Haskell, "How to Make and Use Graphic Charts", 1919)
"Today's scientific investigations are so complicated that even experts in related fields may not understand them well. But there is a logic in the planning of experiments and in the analysis of their results that all intelligent people can grasp, and this logic is a great help in determining when to believe what we hear and read and when to be skeptical. This logic has a great deal to do with statistics, which is why statisticians have a unique interest in the scientific method, and why some knowledge of statistics can so often be brought to bear in distinguishing good arguments from bad ones."
"All things which are proved to be impossible must obviously rest on some assumptions, and when one or more of these assumptions are not true then the impossibility proof fails - but the expert seldom remembers to carefully inspect the assumptions before making their 'impossible' statements." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)
"In an argument between a specialist and a generalist the expert usually wins by simply: (1) using unintelligible jargon, and (2) citing their specialist results which are often completely irrelevant to the discussion. The expert is, therefore, a potent factor to be reckoned with in our society. Since experts are both necessary, and also at times do great harm in blocking significant progress, they need to be examined closely. All too often the expert misunderstands the problem at hand, but the generalist cannot carry though their side to completion. The person who thinks they understand the problem and does not is usually more of a curse (blockage) than the person who knows they do not understand the problem." (Richard Hamming, "The Art of Doing Science and Engineering: Learning to Learn", 1997)
"Know the subject matter, learn it fast, or get a trustworthy expert. To identify the unknown, you must know the known. But don't be afraid to challenge experts on the basis of your logical reasoning. Sometimes a knowledge of the subject matter can blind the expert to the novel or unexpected." (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)
"One reason we tend to accept statistics uncritically is that we assume that numbers come from experts who know what they're doing. [...] There is a natural tendency to treat these figures as straightforward facts that cannot be questioned." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)
"The fact that cognitive diversity matters does not mean that if you assemble a group of diverse but thoroughly uninformed people, their collective wisdom will be smarter than an expert's. But if you can assemble a diverse group of people who possess varying degrees of knowledge and insight, you're better off entrusting it with major decisions rather than leaving them in the hands of one or two people, no matter how smart those people are." (James Surowiecki, "The Wisdom of Crowds", 2005)
"Abstractions matter to users too. Novice users want programs whose abstractions are simple and easy to understand; experts want abstractions that are robust and general enough to be combined in new ways. When good abstractions are missing from the design, or erode as the system evolves, the resulting program grows barnacles of complexity. The user is then forced to master a mass of spurious details, to develop workarounds, and to accept frequent, inexplicable failures." (Daniel Jackson, "Software Abstractions", 2006)
"Much data in databases has a long history. It might have come from old 'legacy' systems or have been changed several times in the past. The usage of data fields and value codes changes over time. The same value in the same field will mean totally different thing in different records. Knowledge or these facts allows experts to use the data properly. Without this knowledge, the data may bc used literally and with sad consequences. The same is about data quality. Data users in the trenches usually know good data from bad and can still use it efficiently. They know where to look and what to check. Without these experts, incorrect data quality assumptions are often made and poor data quality becomes exposed." (Arkady Maydanchik, "Data Quality Assessment", 2007)
"Asking experts to do boring and repetitive, and yet technically demanding tasks is the most certain way of ensuring human error that we can think of, short of sleep deprivation, or inebriation." (David Farley & Jez Humble, "Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation", 2010)
"Experts in the 'Problem' area proceed to elaborate its complexity. They design complex Systems to attack it. This approach guarantees failure, at least for all but the most pedestrian tasks. The problem is a Problem precisely because it is incorrectly conceptualized in the first place, and a large System for studying and attacking the Problem merely locks in the erroneous conceptualization into the minds of everyone concerned. What is required is not a large System, but a different approach. Trying to design a System in the hope that the System will somehow solve the Problem, rather than simply solving the Problem in the first place, is to present oneself with two problems in place of one." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)
confusing, steeped in mystery and only truly understood by a few highly technical experts." (Alan Pennington, "The Customer Experience Book", 2016)
"Data from the customer interactions is the lifeblood for any organisation to view, understand and optimise the customer experience both remotely and on the front line! In the same way that customer experience experts understand that it’s the little things that count, it’s the small data that can make all the difference." (Alan Pennington, "The Customer Experience Book", 2016)
"Feature extraction is also the most creative part of data science and the one most closely tied to domain expertise. Typically, a really good feature will correspond to some real‐world phenomenon. Data scientists should work closely with domain experts and understand what these phenomena mean and how to distill them into numbers." (Field Cady, "The Data Science Handbook", 2017)
"The greatest leaders possess a combination of divergent traits: they are both experts and naïve, creative and efficient, serious and playful, social and reclusive - or at the very least, they surround themselves with this dynamic." (Beau Lotto, "Deviate: The Science of Seeing Differently", 2017)
"We, newbies and young programmers, don't like chaos because it makes us dependent on experts. We have to beg for information and feel bad." (Yegor Bugayenko, "Code Ahead", 2018)
"Data-intensive projects generally involve at least one person who understands all the nuances of the application, process, and source and target data. These are the people who also know about all the abnormalities in the data and the workarounds to deal with them, and are the experts. This is especially true in the case of legacy systems that store and use data in a manner it should not be used. The knowledge is not documented anywhere and is usually inside the minds of the people. When the experts leave, with no one having a true understanding of the data, the data are not used properly and everything goes haywire." (Rupa Mahanti, "Data Quality: Dimensions, Measurement, Strategy, Management, and Governance", 2019)
"I believe that the backlash against statistics is due to four primary reasons. The first, and easiest for most people to relate to, is that even the most basic concepts of descriptive and inferential statistics can be difficult to grasp and even harder to explain. […] The second cause for vitriol is that even well-intentioned experts misapply the tools and techniques of statistics far too often, myself included. Statistical pitfalls are numerous and tough to avoid. When we can't trust the experts to get it right, there's a temptation to throw the baby out with the bathwater. The third reason behind all the hate is that those with an agenda can easily craft statistics to lie when they communicate with us […] And finally, the fourth cause is that often statistics can be perceived as cold and detached, and they can fail to communicate the human element of an issue."
"It is also important to note that data literacy is not about expecting to or becoming an expert; rather, it is a journey that must begin somewhere." (Angelika Klidas & Kevin Hanegan, "Data Literacy in Practice", 2022)
"Expert knowledge is a term covering various types of knowledge that can help define or disambiguate causal relations between two or more variables. Depending on the context, expert knowledge might refer to knowledge from randomized controlled trials, laws of physics, a broad scope of experiences in a given area, and more." (Aleksander Molak, "Causal Inference and Discovery in Python", 2023)
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