Spurious Correlation Quotes

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Racism, at the individual level, can be seen as a predictive model whirring away in billions of human minds around the world. It is built from faulty, incomplete, or generalized data. Whether it comes from experience or hearsay, the data indicates that certain types of people have behaved badly. That generates a binary prediction that all people of that race will behave that same way. Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias.
Cathy O'Neil (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy)
On second thought, this might not be spurious. Computer science doctorates vs. Comic book sales
Tyler Vigen (Spurious Correlations)
Unsurprisingly, injuries related to falling televisions have an even stronger correlation to television sales. That graph does not appear in this book, but look for it in my upcoming sequel: Not-So-Spurious Correlations.
Tyler Vigen (Spurious Correlations)
1.11 Why Do Myths About Intelligence Definitions and Measurement Persist? Given all this strong empirical evidence that intelligence test scores are meaningful, why does the myth persist that scores have little if any validity? Here is an informative example. From time to time, a college admissions representative will assert that in their institution they find no relationship between grade point average (GPA) and SAT scores. Such observations are virtually always based on a lack of understanding of a basic statistical principle regarding the correlation between two variables. To calculate a correlation between any two variables, there must be a wide range of scores for each variable. At a place like MIT, for example, most students fall in a narrow range of high SAT scores. This is a classic problem of restriction of range. There is little variance among the students, so in this case, the relationship between GPA and SAT scores will not be very strong. Sampling from just the high end or just the low end or just the middle of a distribution restricts range and results in spuriously low or zero correlations. Restriction of range actually accounts for many findings about what intelligence test scores “fail” to predict.
Richard J. Haier (The Neuroscience of Intelligence (Cambridge Fundamentals of Neuroscience in Psychology))
To avoid getting fooled by spurious correlations, we need to consider additional variables that would be expected to change if a particular causal explanation were true. Twenge does this by examining all the daily activities reported by individual students, in the two datasets that include such measures. Twenge finds that there are just two activities that are significantly correlated with depression and other suicide-related outcomes (such as considering suicide, making a plan, or making an actual attempt): electronic device use (such as a smartphone, tablet, or computer) and watching TV. On the other hand, there are five activities that have inverse relationships with depression (meaning that kids who spend more hours per week on these activities show lower rates of depression): sports and other forms of exercise, attending religious services, reading books and other print media, in-person social interactions, and doing homework.
Jonathan Haidt (The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure)
The mental health field also maintains authority through selectivity of its members and suppressed dissent. There is a pretense of certainty propagated by leaders in mental health, with oft repeated promises of supporting evidence to be discovered soon; it is taken for granted that their authoritative stance is merited. Despite this political posturing, several areas of concern actually leave much to question, for instance: it is rare for findings to be replicated (Open Science Collaboration, 2015), with only about 3% of journals even being willing to accept articles attempting to repeat previous studies to see if their findings were more than just a fluke (Martin & Clarke, 2017); the peer -review process of journals is biased toward recognizable names and against newcomers or detractors (Bravo, Farjam, Grimaldo Moreno, Birukou, & Squazzoni, 2018), setting up a sort of “good ol’ boys’ club” dynamic; the rates of authors retracting their studies due to problems or false findings are rapidly rising (Steen, Casadevall, & Fang, 2013); the subjects used in studies are consistently biased (Nielsen, Haun, Kartner, & Legare, 2017) and based on samples that are among the least representative of humans, in general (e.g., Arnett, 2008); spurious and meaningless correlations are frequently reported as exciting new discoveries (see Richardson, 2017); gold-standard “evidence-based treatments” are, on average and at best, only helpful for about 25% of people (Shedler, 2015); selective reporting, guild interests, and researcher allegiance heavily bias psychiatric research (Leichsenring et al., 2017; Whitaker & Cosgrove, 2015); and, perhaps most important, with all the purported advances in treatment, the prevalence and long-term outcomes of diagnosable mental disorders has not decreased in the last century (Jorm, Patten, Brugha, & Mojtabai, 2017; Margraf & Schneider, 2016), while disability rates continue to rise exponentially (see Whitaker, 2010 for an analysis on this trend).
Noel Hunter (Trauma and Madness in Mental Health Services)
The difficulty rating of words in the Scripps National Spelling Bee is based on a very complicated mathematical algorithm involving a linguistical analysis of the origin of the word, the syllables, and expected phonetic construction. Just kidding. A couple of wordsmiths sit around a table and throw out difficulty ratings like they’re judging a swan dive.
Tyler Vigen (Spurious Correlations)
According to a scientist at the National Severe Storms Laboratory, a tornado would most likely fillet a shark. On a related note, this would be the first entry in my new idea for a cookbook: Statistically Unlikely Recipes.
Tyler Vigen (Spurious Correlations)
If you don’t find a particular graph interesting, please blame the students of Harvard Law School.
Tyler Vigen (Spurious Correlations)
New sociology PhDs realize there is no work in their field, take jobs as astronauts instead. Sociology doctorates vs. Worldwide noncommercial space launches
Tyler Vigen (Spurious Correlations)
And most big data programs do a poor job of identifying which correlations are more or less likely to be spurious. The use of big data to draw inferences that should be evaluated and tested is often neglected in favor of using big data to produce real-time transactions—
Alec J. Ross (The Industries of the Future)
Another major bias in observational studies is confounding. As stated, confounding occurs when a third variable is correlated with both the exposure and outcome. If the third variable is not taken into consideration, a spurious relationship between the
Mit Critical Data (Secondary Analysis of Electronic Health Records)
Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias
Cathy O'Neil (Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy)