Posts Tagged ‘statistics’
“Losing my religion”*…
Shifting religious affiliations in the U.S. have generated lots of comment (e.g., Friday’s New York Times: “The Christian Right Is in Decline, and It’s Taking America With It“). It’s worth taking a comprehensive look at the data on which those takes are based; there’s even more to see…
Seven in ten Americans (70%) identify as Christian, including more than four in ten who identify as white Christian and more than one-quarter who identify as Christian of color. Nearly one in four Americans (23%) are religiously unaffiliated, and 5% identify with non-Christian religions.
The most substantial cultural and political divides are between white Christians and Christians of color. More than four in ten Americans (44%) identify as white Christian, including white evangelical Protestants (14%), white mainline (non-evangelical) Protestants (16%), and white Catholics (12%), as well as small percentages who identify as Latter-day Saint (Mormon), Jehovah’s Witness, and Orthodox Christian. Christians of color include Hispanic Catholics (8%), Black Protestants (7%), Hispanic Protestants (4%), other Protestants of color (4%), and other Catholics of color (2%). The rest of religiously affiliated Americans belong to non-Christian groups, including 1% who are Jewish, 1% Muslim, 1% Buddhist, 0.5% Hindu, and 1% who identify with other religions. Religiously unaffiliated Americans comprise those who do not claim any particular religious affiliation (17%) and those who identify as atheist (3%) or agnostic (3%).
Over the last few decades, the proportion of the U.S. population that is white Christian has declined by nearly one-third. As recently as 1996, almost two-thirds of Americans (65%) identified as white and Christian. By 2006, that had declined to 54%, and by 2017 it was down to 43%. The proportion of white Christians hit a low point in 2018, at 42%, and rebounded slightly in 2019 and 2020, to 44%. That tick upward indicates the decline is slowing from its pace of losing roughly 11% per decade.
The slight increase in white Christians between 2018 and 2020 was driven primarily by an uptick in the proportion of white mainline (non-evangelical) Protestants and a stabilization in the proportion of white Catholics. Since 2007, white mainline (non-evangelical) Protestants have declined from 19% of the population to a low of 13% in 2016, but the last three years have seen small but steady increases, up to 16% in 2020. White Catholics have also declined from a high point of 16% of the population in 2008, and their low point of 11% occurred in 2018. It is unclear if the bump back up to 12% in 2020 indicates a new trend.
Since 2006, white evangelical Protestants have experienced the most precipitous drop in affiliation, shrinking from 23% of Americans in 2006 to 14% in 2020. That proportion has generally held steady since 2017 (15% in 2017, 2018, and 2019).
Disaffiliating white Christians have fueled the growth of the religiously unaffiliated during this period. Only 16% of Americans reported being religiously unaffiliated in 2007; this proportion rose to 19% by 2012, and then gained roughly a percentage point each year from 2012 to 2017. Reflecting the patterns above, the proportion of religiously unaffiliated Americans hit a high point of 26% in 2018 but has since slightly declined, to 23% in 2020.
The increase in proportion of religiously unaffiliated Americans has occurred across all age groups but has been most pronounced among young Americans. In 1986, only 10% of those ages 18–29 identified as religiously unaffiliated. In 2016, that number had increased to 38%, and declined slightly in 2020, to 36%.
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Americans ages 18–29 are the most religiously diverse age group. Although a majority (54%) are Christian, only 28% are white Christians (including 12% who are white mainline Protestants, 8% who are white Catholics, and 7% who are white evangelical Protestants), while 26% are Christians of color (including 9% who are Hispanic Catholics, 5% who are Hispanic Protestants, 5% who are Black Protestants, 2% who are multiracial Christians, 2% who are AAPI Christians, and 1% who are Native American Christians). More than one-third of young Americans (36%) are religiously unaffiliated, and the remainder are Jewish (2%), Muslim (2%), Buddhist (1%), Hindu (1%), or another religion (1%).
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Americans ages 65 and older are the only group whose religious profile has changed significantly since 2013. Among Americans 65 and older, the proportion of white evangelical Protestants dropped from 26% in 2013 to 22% in 2020, and the proportion of white Catholics dropped from 18% in 2013 to 15% in 2020. By contrast, the proportion of religiously unaffiliated seniors increased from 11% in 2013 to 14% in 2020.
White evangelical Protestants are the oldest religious group in the U.S., with a median age of 56, compared to the median age in the country of 47. White Catholics and Unitarian Universalists have median ages of 54 and 53 years old, respectively. Black Protestants and white mainline Protestants have a median age of 50. All other groups have median ages below 50: Jehovah’s Witnesses (49), Jewish Americans (48), Latter-day Saints (47), Orthodox Christians (42), Hispanic Catholics (42), Hispanic Protestants (39), religiously unaffiliated people (38), Buddhists (36), Hindus (36), and Muslims (33). In the youngest groups, one-third of Hindu (33%) and Buddhist (34%) Americans and 42% of Muslim Americans are in the 18–29 age category.
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Delving into the data of devotion: “The American Religious Landscape in 2020.“
* R.E.M.
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As we ponder piety, we might send evangelical birthday greetings to Bardaisan; he was born on this date in 154. A scientist, scholar, astrologer, philosopher, hymnographer, and poet, he was the first known Syriac literary author. A key figure among the Gnostics, he founded the Bardaisanites and was central to the Christianization of Rome (indeed, he is said to have converted prince Abgar IX).
“An imbalance between rich and poor is the oldest and most fatal ailment of all republics”*…
… so, how we measure it matters…
In 2015, Greece, Thailand, Israel, and the UK were equally unequal. That is, all four countries had the same Gini coefficient, a common measure of income inequality.
The number suggests that the spread of incomes in the four nations was the same. However, a close look at the poorest and wealthiest in those societies reveals a very different picture. The ratio between income held by the richest 10% and the poorest 10% ranged significantly, from 13.8 in Greece to 4.2 in the UK.
The fact is, just because the Gini coefficient is so well known doesn’t mean it’s a particularly useful measurement. Its appeal comes from its simplicity—a number between 0 and 1 that can encapsulate a complex distribution in a single figure—as well as its popularity. It is also regularly published and updated by powerful international organizations like the OECD, the World Bank, and the International Monetary Fund.
However, it has a number of serious limitations. So many, in fact, that the World Inequality Database, one of the world’s leading sources of income inequality data, steers clear. And it’s not alone. While some economists defend the Gini coefficient’s continued use, most agree that as a way to understand income inequality, it’s insufficient on its own…
A primer on the dominant measure of economic inequality, and on some alternatives/supplements to it: “Gini coefficient: An introduction.”
* Plutarch
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As we aim to understand, we might note that today is the Summer Solstice, the day on which the earth’s north pole is maximally tilted toward sun, and there are more hours of daylight than on any other day of the year (in the Northern Hemisphere; in the Southern, it is the Winter Solstice, the shortest day). The June solstice is the only day of the year when all locations inside the Arctic Circle experience a continuous period of daylight for 24 hours. And perhaps more immediately, it is the “official” start of Summer.
(The 21st is the traditional date; in the event, the solstice falls on the 20th, 21st, or 22nd– this year, on the 20th… still, the traditional date is the one folks tend to mark.)
Not coincidentally, today is also National Daylight Appreciation Day.
“Gentlemen, you need to add armor-plate where the holes aren’t, because that’s where the holes were on the airplanes that didn’t return”*…
Allied bombers were key to Britain’s air offensive against Germany during the second world war. As such, the RAF wanted to armour their bombers to prevent them from being shot down. But armour is heavy – you cannot reinforce an entire bomber and still have it fly. So statistician Abraham Wald was asked to advise on where armour should be placed on a bomber.
After each wave of bombing, every returning aircraft was meticulously examined and a note was made of where each aircraft had sustained damage by the Germans. The image [above] conceptualises what Wald’s data might have looked like visually.
So what was Wald’s advice? Where should armour be added?
He essentially advised the RAF to add armour to places where you do not find bullet holes. Wait… what?!
Wald wisely understood that the data was based only on planes that survived. The planes that did not survive were likely to have sustained damage on the areas where we do not observe bullet holes – such as around the engine or cockpit…
Making better decisions: one of the most prevalent– and insidious– forms of selection bias, survivorship bias, illustrated: “How to armour a WWII bomber.”
See also: “How to avoid being duped by survivorship bias.”
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As we think clearly, we might send productive birthday greetings to W. Edwards Deming; he was born on this date in 1900. An engineer, statistician, professor, author, lecturer, and management consultant, he helped develop the sampling techniques still used by the U.S. Department of the Census and the Bureau of Labor Statistics.
But he is better remembered as the champion of statistically-based production management techniques that first gained traction in post-WWII Japan, where many credit Deming as a key ingredient in what has become known as the Japanese post-war economic miracle of 1950 to 1960, when Japan rose from the ashes of war onto the its path to becoming the second-largest economy in the world– through processes shaped by the ideas Deming taught. In 1951, the Japanese government established the Deming Prize in his honor.
While his impact in Japan (finally) brought him to the attention of business leaders in the U.S., he was only just beginning to win widespread recognition in the U.S. at the time of his death in 1993.
“Oh, I am fortune’s fool!”*…
For many years, my life centered around studying the biases of human decision-making: I was a graduate student in psychology at Columbia, working with that marshmallow-tinted legend, Walter Mischel, to document the foibles of the human mind as people found themselves in situations where risk abounded and uncertainty ran high. Dissertation defended, I thought to myself, that’s that. I’ve got those sorted out. And in the years that followed, I would pride myself on knowing so much about the tools of self-control that would help me distinguish myself from my poor experimental subjects. Placed in a stochastic environment, faced with stress and pressure, I knew how I’d go wrong — and I knew precisely what to do when that happened.
Fast-forward to 2016. I have embarked on my latest book project, which has taken me into foreign territory: the world of No Limit Texas Hold ’em… The biases I know all about in theory, it turns out, are much tougher to fight in practice…
Maria Konnikova. a New York Times bestselling author and contributor to The New Yorker with a doctorate in psychology, decided to learn how to play poker to better understand the role of luck in our lives, examining the game through the lens of psychology and human behavior. An excerpt is adapted from her new book, The Biggest Bluff: How I Learned to Pay Attention, Master Myself, and Win: “The Hard Truth Of Poker — And Life: You’re Never ‘Due’ For Good Cards.”
* Shakespeare, Romeo and Juliet
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As we ante up, we might spare a thought for Don Featherstone; he died on this date in 2015. An artist, he is surely best remembered for his creation of the plastic pink flamingo lawn ornament in 1957, while working for Union Products. It went on sale the following year– and now adorns lawns nationwide.
In 1996, Featherstone was awarded the 1996 Ig Nobel Art Prize for his creation; that same year, he began his tenure as president of Union Products, a position he held until he retired in 2000.
A Featherstone flock
“There are three types of lies — lies, damn lies, and statistics”*…
“Hiding in Plain Sight”
A chart’s purpose is usually to help you properly interpret data. But sometimes, it does just the opposite. In the right (or wrong) hands, bar graphs and pie charts can become powerful agents of deception, tricking you into inferring trends that don’t exist, mistaking less for more, and missing alarming facts. The best measure of a chart’s honesty is the amount of time it takes to interpret it, says Massachusetts Institute of Technology perceptual scientist Ruth Rosenholtz: “A bad chart requires more cognitive processes and more reasoning about what you’ve seen.”…
Five examples (like the one above) of the kinds of tricks that charts can try to pull, explained: “Five Ways to Lie with Charts.”
* Benjamin Disraeli
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As we stack the deck, we might recall that it was on this date in 2010, at 2:32p EDT, that the U.S. stock markets suffered a “Flash Crash”– in a period of just 36 minutes, the S&P 500, Dow Jones Industrial Average, and Nasdaq Composite collapsed and rebounded (the Dow, e.g., lost 9% of its value, then recovered most of it).
Nearly five years later, the SEC charged a 36-year-old small-time trader who worked from his parents’ modest stucco house in suburban west London with having caused the collapse (using spoofing and layering, along with a form of front-running– all now explicitly outlawed). But many experts are not convinced; to this day, there are numerous theories– but no consensus– as to the cause(s) of the crash.
The DJIA on May 6, 2010 (11:00 AM – 4:00 PM EDT)
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