The Art of Statistics Quotes

“Even in an era of open data, data science and data journalism, we still need basic statistical principles in order not to be misled by apparent patterns in the numbers.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Signals always come with noise: It is trying to separate out the two that makes the subject interesting.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“More data means that we need to be even more aware of what the evidence is actually worth.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Far from freeing us from the need for statistical skills, bigger data and the rise in the number and complexity of scientific studies makes it even more difficult to draw appropriate conclusions.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. — Nate Silver, The Signal and the Noise1”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“When the CERN teams reported a 'five-sigma' result for the Higgs boson, corresponding to a P-value of around 1 in 3.5 million, the BBC reported the conclusion correctly, saying this meant 'about a one-on-3.5 million chance that the signal they see would appear if there were no Higgs particle.' But nearly every other outlet got the meaning of this P-value wrong. For example, Forbes Magazine reported, 'The chances are less than 1 in a million that it is not the Higgs boson,' a clear example of the prosecutor's fallacy. The Independent was typical in claiming that 'there is less than a one in a million chance that their results are a statistical fluke.' This may not be blatantly mistaken as Forbes, but it is still assigning the small probability to 'their results are a statistical fluke', which is logically the same as saying this is the probability of the null hypothesis being tested.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“We need to distinguish what is actually dangerous from what sounds frightening.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“All models are wrong, but some are useful.’ CHAPTER 6 Algorithms, Analytics and Prediction”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Even after my decades as a statistician, when asked a basic school question using probability, I have to go away, sit in silence with a pen and paper, try it a few different ways, and finally announce what I hope is the correct answer.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Alberto Cairo has identified four common features of a good data visualization: It contains reliable information. The design has been chosen so that relevant patterns become noticeable. It is presented in an attractive manner, but appearance should not get in the way of honesty, clarity and depth. When appropriate, it is organized in a way that enables some exploration.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Probability theory naturally comes into play in what we shall call situation 1: When the data-point can be considered to be generated by some randomizing device, for example when throwing dice, flipping coins, or randomly allocating an individual to a medical treatment using a pseudo-random-number generator, and then recording the outcomes of their treatment. But in practice we may be faced with situation 2: When a pre-existing data-point is chosen by a randomizing device, say when selecting people to take part in a survey. And much of the time our data arises from situation 3: When there is no randomness at all, but we act as if the data-point were in fact generated by some random process, for example in interpreting the birth weight of our friend’s baby.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“if we do ten trials of useless drugs the chance of getting at least one significant at P < 0.05 gets as high as 40%.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“Most drugs on the market have only moderate effects, and only help a minority of people”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“there is no substitute for simply looking at data properly,”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“These examples show that statistics are always to some extent constructed on the basis of judgements, and it would be an obvious delusion to think the full complexity of personal experience can be unambiguously coded and put into a spreadsheet or other software.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“For example, in 2017 budget airline Ryanair announced that 92% of their passengers were satisfied with their flight experience. It turned out that their satisfaction survey only permitted the answers, ‘Excellent, very good, good, fair, OK’.fn2”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Hans Rosling,”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“turn experience into data, we have to start with rigorous definitions.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“The first rule of communication is to shut up and listen,”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“Of course this attempt at scientific objectivity is easier said than done. When the Statistical Society of London (later the Royal Statistical Society) was set up in 1834 by Charles Babbage, Thomas Malthus and others, they loftily declared that ‘The Statistical Society will consider it to be the first and most essential rule of its conduct to exclude carefully all opinions from its transactions and publications – to confine its attention rigorously to facts – and, as far as it may be found possible, to facts which can be stated numerically and arranged in tables.’⁷ From the very start they took no notice whatsoever of this stricture, and immediately starting inserting their opinions about what their data on crime, health and the economy meant and what should be done in response to it. Perhaps the best we can do now is recognize this temptation and do our best to keep our opinions to ourselves.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“So for a survey of 1,000 people (the industry standard), the margin of error is generally quoted as ± 3%:fn8 if 400 of them said they preferred coffee, and 600 of them said they preferred tea, then you could roughly estimate the underlying percentage of people in the population who prefer coffee as 40 ± 3%, or between 37% and 43%. Of course, this is only accurate if the polling company really did take a random sample, and everyone replied, and they all had an opinion either way and they all told the truth. So although we can calculate margins of error, we must remember that they only hold if our assumptions are roughly correct. But can we rely on these assumptions?”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“this book is part of what could be called a new wave in statistics teaching, in which formal probability theory as a basis for statistical inference does not come in till much later”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“The British statistician George Box has become famous for his brief but invaluable aphorism: ‘All models are wrong, some are useful.’ This pithy statement was based on a lifetime spent bringing statistical expertise to industrial processes, which led Box to appreciate both the power of models, but also the danger of actually starting to believe in them too much.”
― David Spiegelhalter, The Art of Statistics: Learning from Data

“We have seen the problems that result when researchers only report significant findings, but perhaps more important are the conscious or unconscious set of minor decisions that might be made by the researcher depending on what the data seem to be showing. These 'tweaks' might include decisions about changes in the design of the experiment, when to stop collecting data, what data to exclude, what factors to adjust for, what groups to emphasize, what outcome measures to focus on, how to split continuous variables into groups, how to handle missing data, and so on. Simonsohn calls these decisions 'researcher degrees of freedom', while Andrew Gelman refers more poetically to the 'garden of forking paths'.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“[Adolphe Quetelet] developed the idea of 'social physics', since the regularity of societal statistics seemed to reflect an almost mechanistic underlying process. Just as the random molecules of a gas come together to make predictable physical properties, so the unpredictable workings of millions of individual lives come together to produce, for example, national suicide rates that barely change from year to year.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“Worry about data quality: Everything rests on the data.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“What am I not being told? This is perhaps the most important question of all.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“shorter mothers tend to have taller daughters.”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“Does going to university increase the risk of getting a brain tumour?”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data

“it is almost always an imperfect measure of what we are really interested in:”
― David Spiegelhalter, The Art of Statistics: How to Learn from Data