"When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely in your thoughts advanced to the state of science.” (Lord Kelvin, "Electrical Units of Measurement", 1883)
“Of itself an arithmetic average is more likely to conceal than to disclose important facts; it is the nature of an abbreviation, and is often an excuse for laziness.” (Arthur Lyon Bowley, “The Nature and Purpose of the Measurement of Social Phenomena”, 1915)
“Science depends upon measurement, and things not measurable are therefore excluded, or tend to be excluded, from its attention.” (Arthur J Balfour, “Address”, 1917)
“It is important to realize that it is not the one measurement, alone, but its relation to the rest of the sequence that is of interest.” (William E Deming, “Statistical Adjustment of Data”, 1943)
“The purpose of computing is insight, not numbers […] sometimes […] the purpose of computing numbers is not yet in sight.” (Richard Hamming, “Numerical Methods for Scientists and Engineers”, 1962)
“A quantity like time, or any other physical measurement, does not exist in a completely abstract way. We find no sense in talking about something unless we specify how we measure it. It is the definition by the method of measuring a quantity that is the one sure way of avoiding talking nonsense...” (Hermann Bondi, “Relativity and Common Sense”, 1964)
“Measurement, we have seen, always has an element of error in it. The most exact description or prediction that a scientist can make is still only approximate.” (Abraham Kaplan, “The Conduct of Inquiry: Methodology for Behavioral Science”, 1964)
“A mature science, with respect to the matter of errors in variables, is not one that measures its variables without error, for this is impossible. It is, rather, a science which properly manages its errors, controlling their magnitudes and correctly calculating their implications for substantive conclusions.” (Otis D Duncan, “Introduction to Structural Equation Models”, 1975)
“Data in isolation are meaningless, a collection of numbers. Only in context of a theory do they assume significance […]” (George Greenstein, “Frozen Star”, 1983)
"Changing measures are a particularly common problem with comparisons over time, but measures also can cause problems of their own. [...] We cannot talk about change without making comparisons over time. We cannot avoid such comparisons, nor should we want to. However, there are several basic problems that can affect statistics about change. It is important to consider the problems posed by changing - and sometimes unchanging - measures, and it is also important to recognize the limits of predictions. Claims about change deserve critical inspection; we need to ask ourselves whether apples are being compared to apples - or to very different objects." (Joel Best, "Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists", 2001)
"Measuring anything subjective always prompts perverse behavior. [...] All measurement systems are subject to abuse." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)
“The value of having numbers - data - is that they aren't subject to someone else's interpretation. They are just the numbers. You can decide what they mean for you.” (Emily Oster, “Expecting Better”, 2013)
"Until a new metric generates a body of data, we cannot test its usefulness. Lots of novel measures hold promise only on paper." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)
"Usually, it is impossible to restate past data. As a result, all history must be whitewashed and measurement starts from scratch." (Kaiser Fung, "Numbersense: How To Use Big Data To Your Advantage", 2013)
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