Best 232 quotes in «statistics quotes» category

[I]t would be a niceness that was enforced leniently, patiently and gracefully, with the sort of unflappable self-certainty [they] couldn't help displaying when all its statistics proved that it really was doing the right thing.

Maar wie het zeker weet, is per definitie niet nieuwsgierig. Wie koste wat het kost vasthoudt aan zijn overtuigingen, staat nooit open voor nieuwe informatie. Willen we goed met cijfers – en informatie in het algemeen – omgaan, dan zullen we die onzekerheid moeten omarmen.

Love doesn't read statistics!

Many a statistic is false on its face. It gets by only because the magic of numbers brings about a suspension of common sense.

Nature has established patterns originating in the return of events, but only for the most part. New illnesses flood the human race, so that no matter how many experiments you have done on corpses, you have not thereby immposd a limit on the nature of events so that in the future they could not vary.

More data means more information, but it also means more false information.

One is born an individual; one becomes a statistic.

Out of every one hundred Americans, fewer than two get aid from today’s cash welfare program. Just 27 percent of poor families with children participate. There are more avid postage stamp collectors in the United States than welfare recipients.

Outside of the killings, DC has one of the lowest crime rates in the country.

Regression analysis is the hydrogen bomb of the statistics arsenal.

Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns- the ones we don't know we don't know.

Scientists and inventors of the USA (especially in the so-called "blue state" that voted overwhelmingly against Trump) have to think long and hard whether they want to continue research that will help their government remain the world's superpower. All the scientists who worked in and for Germany in the 1930s lived to regret that they directly helped a sociopath like Hitler harm millions of people. Let us not repeat the same mistakes over and over again.

Longitudinal data sets are the research equivalent of a Ferrari. Not surprisingly, we can't always have the Ferrari. The research equivalent of a Toyota is a cross-sectional data set.

Numbers never lie, after all: they simply tell different stories depending on the math of the tellers.

One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten.

Our statistics are at fault: the population has been returned too large. How many men are there to a square thousand miles in this country? Hardly one.

Over 50 percent of marriages end in divorce. Many people aren't even getting married to avoid that statistic. I contend that you get out of a marriage what you put into it. Make it a priority to date your wife, to take care of her needs, and grow the bond between you.

Police not enforcing laws results in a high crime rate that is formally reported as a low crime rate in police statistics.

Researchers may have some conscious or unconscious bias, either because of a strongly held prior belief or because a positive finding would be better for their career. (No one ever gets rich or famous by proving what doesn't cause cancer.)

Significance unfortunately is a useful means toward a personal ends in the advance of science - status and widely distributed publications, a big laboratory, a staff of research assistants, a reduction in teaching load, a better salary, the finer wines of Bordeaux. Precision, knowledge, and control. In a narrow and cynical sense statistical significance is the way to achieve these. Design experiment. Then calculate statistical significance. Publish articles showing "significant" results. Enjoy promotion. But it is not science, and it will not last.

Skepticism is always a good first response.

Statistical malfeasance has very little to do with bad math. Judgement an integrity turn out to be surprisingly important. A detailed knowledge of statistics does not deter wrongdoing any more than a detailed knowledge of the law averts criminal behavior.

Statistics cannot be any smarter than the people who use them. And in some cases, they can make smart people do dumb things.

Stats don't measure an athlete's hunger.

...that realisation that I was the oddity, the statistical probability, life was predictable.

The average human being follows the statistics but the success minded human being changes the face of the statistics.

Sloane slipped an arm around my waist. "There are fourteen varieties of hugs," she said. "This is one of them.

Some of the greatest masterpieces of art are created against the odds of reality. from the book: stuff i think about

Statistics are human beings with the tears wiped off.

Statistics don't determine whether you find love or get married. You do!

Statistical fact: cops will never pull over a man in a sweet van if he’s carrying forty pounds of sinsemilla buds. Another fact: ninety percent of all statistics are made up.

Statistics say that a range of mental disorders affects more than one in four Americans in any given year. That means millions of Americans are totally batshit. but having perused the various tests available that they use to determine whether you're manic depressive. OCD, schizo-affective, schizophrenic, or whatever, I'm surprised the number is that low. So I have gone through a bunch of the available tests, and I've taken questions from each of them, and assembled my own psychological evaluation screening which I thought I'd share with you. So, here are some of the things that they ask to determine if you're mentally disordered 1. In the last week, have you been feeling irritable? 2. In the last week, have you gained a little weight? 3. In the last week, have you felt like not talking to people? 4. Do you no longer get as much pleasure doing certain things as you used to? 5. In the last week, have you felt fatigued? 6. Do you think about sex a lot? If you don't say yes to any of these questions either you're lying, or you don't speak English, or you're illiterate, in which case, I have the distinct impression that I may have lost you a few chapters ago.

Statistics show that the nature of English crime is reverting to its oldest habits. In a country where so many desire status and wealth, petty annoyances can spark disproportionately violent behaviour. We become frustrated because we feel powerless, invisible, unheard. We crave celebrity, but that’s not easy to come by, so we settle for notoriety. Envy and bitterness drive a new breed of lawbreakers, replacing the old motives of poverty and the need for escape. But how do you solve crimes which no longer have traditional motives?

That so far the material has been dealt with in a rather subjective way provokes the question whether a means can be found of handling it objectively. [...] This chapter considers the applicability of the statistical tests employed by Wilson and the general problem whether the Linear B data are suited to statistical analysis.

The beauty of the normal distribution - its Michael Jordan power, finesse, and elegance - comes from the fact that we know by definition exactly what proportion of the observations in a normal distribution lie within one standard deviation of the mean (68.2 percent), within two standard deviations of the mean (95.4 percent), within three standard deviations of the mean (99.7 percent), and so on.

The finest studies are like the finest of anything else: They cost big bucks.

The combination of Bayes and Markov Chain Monte Carlo has been called "arguably the most powerful mechanism ever created for processing data and knowledge." Almost instantaneously MCMC and Gibbs sampling changed statisticians' entire method of attacking problems. In the words of Thomas Kuhn, it was a paradigm shift. MCMC solved real problems, used computer algorithms instead of theorems, and led statisticians and scientists into a worked where "exact" meant "simulated" and repetitive computer operations replaced mathematical equations. It was a quantum leap in statistics.

The mind is more comfortable in reckoning probabilities in terms of the relative frequency of remembered or imagined events. That can make recent and memorable events - a plane crash, a shark attack, an anthrax infection - loom larger on one's worry list than more frequent and boring events, such as the car crashes and ladder falls that get printed beneath the fold on page B14. And it can lead risk experts to speak one language and ordinary people to hear another. In hearings for a proposed nuclear waste site, an expert might present a fault tree that lays out the conceivable sequences of events by which radioactivity might escape. For example, erosion, cracks in the bedrock, accidental drilling, or improper sealing might cause the release of radioactivity into groundwater. In turn, groundwater movement, volcanic activity, or an impact of a large meteorite might cause the release of radioactive wastes into the biosphere. Each train of events can be assigned a probability, and the aggregate probability of an accident from all the causes can be estimated. When people hear these analyses, however, the are not reassured but become more fearful than ever. They hadn't realized there are so many ways for something to go wrong! They mentally tabulate the number of disaster scenarios, rather than mentally aggregating the probabilities of the disaster scenarios.

The most important thing to know about statistics is that you don't have to be a statistic.

The non-scientist in the street probably has a clearer notion of physics, chemistry and biology than of statistics, regarding statisticians as numerical philatelists, mere collector of numbers.

...the facts of economic life cannot be comprehensively described in terms of statistics.

The obvious cure for the tragic shortcomings of human intuition in a high-tech world is education. And this offers priorities for educational policy: to provide students with the cognitive tools that are most important for grasping the modern world and that are most unlike the cognitive tools they are born with. The perilous fallacies we have seen in this chapter, for example, would give high priority to economics, evolutionary biology, and probability and statistics in any high school or college curriculum. Unfortunately, most curricula have barely changed since medieval times, and are barely changeable because no one wants to be the philistine who seems to be saying that it is unimportant to learn a foreign language, or English literature, or trigonometry, or the classics. But no matter how valuable a subject may be, there are only twenty-four hours in a day, and a decision to teach one subject is also a decision not to teach another one. The question is not whether trigonometry is important, but whether it is more important than statistics; not whether an educated person should know the classics, but whether it is more important for an educated person to know the classics than to know elementary economics. In a world whose complexities are constantly challenging our intuitions, these trade-offs cannot responsibly be avoided.

The only meaningful statistic in warfare is when the other side quits.

The USA disability statistics clearly show that the sordid system abandons the majority of its sickened applicants in their time of greatest need.

The USA utility power generation industry subcontracts out their dangerous jobs so that the bad statisitics will not be associated with them. Smart people avoid working for the subcontractors. I have worked directly for a number of subcontractors and overseen subcontractors and their staff were clearly sick, showing behavioral problems and overworked. In some cases they were blatantly breaking OSHA laws. OSHA covers it all up! Unfortunately, the problems can be traced back to OSHA and their wilful lack of enforcement of the law.

This book is an essay in what is derogatorily called "literary economics," as opposed to mathematical economics, econometrics, or (embracing them both) the "new economic history." A man does what he can, and in the more elegant - one is tempted to say "fancier" - techniques I am, as one who received his formation in the 1930s, untutored. A colleague has offered to provide a mathematical model to decorate the work. It might be useful to some readers, but not to me. Catastrophe mathematics, dealing with such events as falling off a height, is a new branch of the discipline, I am told, which has yet to demonstrate its rigor or usefulness. I had better wait. Econometricians among my friends tell me that rare events such as panics cannot be dealt with by the normal techniques of regression, but have to be introduced exogenously as "dummy variables." The real choice open to me was whether to follow relatively simple statistical procedures, with an abundance of charts and tables, or not. In the event, I decided against it. For those who yearn for numbers, standard series on bank reserves, foreign trade, commodity prices, money supply, security prices, rate of interest, and the like are fairly readily available in the historical statistics.

The value for which P=0.05, or 1 in 20, is 1.96 or nearly 2; it is convenient to take this point as a limit in judging whether a deviation ought to be considered significant or not. Deviations exceeding twice the standard deviation are thus formally regarded as significant. Using this criterion we should be led to follow up a false indication only once in 22 trials, even if the statistics were the only guide available. Small effects will still escape notice if the data are insufficiently numerous to bring them out, but no lowering of the standard of significance would meet this difficulty.

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.

Uczeni wyliczyli, że jest tylko jedna szansa na milion, by zaistniało coś tak całkowicie absurdalnego. Jednak magowie obliczyli, że szanse jedna na milion sprawdzają się w dziewięciu przypadkach na dziesięć.

We encounter regression to the mean almost every day of our lives. We should try to anticipate it, recognize it, and not be fooled by it.