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Millennials Are Screwed - The Huffington Post
There are millions of Scotts in the modern economy. “A lot of workers were just 18 at the wrong time,” says William Spriggs, an economics professor at Howard University and an assistant secretary for policy at the Department of Labor in the Obama administration. “Employers didn’t say, ‘Oops, we missed a generation. In 2008 we weren’t hiring graduates, let’s hire all the people we passed over.’ No, they hired the class of 2012.”
today  youth  housing  millennials  politics  usa  numbers  statistics  workforce 
february 2018 by aries1988
Hans Rosling, physician and statistician, 1948-2017

Rosling, who has died aged 68, was the closest thing statisticians had to a rock star. His most famous talk, 2006’s The Best Stats You’ve Ever Seen, has been watched online more than 11m times. Its ambitious scope and sweeping narrative epitomised Rosling’s ability to rise above the ebb and flow of current affairs and see generational trends.

It led him to conclude that on most measures of human progress — the impact of climate change being a notable exception — most countries were improving rapidly.

Public perceptions had not kept up with the pace of economic and demographic development and much of the media was hobbled by its adherence to false balance, he said. The world is discussed in terms of feelings and ideologies rather than as an area of knowledge, he once told the Financial Times.
leader  statistics  communication  science  world  data  debate  politics  ideology  optimism  explained  population 
december 2017 by aries1988
How statistics lost their power – and why we should fear what comes next | William Davies

In France, it has been illegal to collect census data on ethnicity since 1978, on the basis that such data could be used for racist political purposes. (This has the side-effect of making systemic racism in the labour market much harder to quantify.)

Speaking scientifically about the nation – for instance in terms of macroeconomics – is an insult to those who would prefer to rely on memory and narrative for their sense of nationhood, and are sick of being told that their imagined community does not exist.

the geography of capitalism has been pulling in somewhat different directions. Plainly globalisation has not rendered geography irrelevant. In many cases it has made the location of economic activity far more important, exacerbating the inequality between successful locations (such as London or San Francisco) and less successful locations (such as north-east England or the US rust belt). The key geographic units involved are no longer nation states. Rather, it is cities, regions or individual urban neighbourhoods that are rising and falling.

Immigration may be good for the economy overall, but this does not mean that there are no local costs at all. So when politicians use national indicators to make their case, they implicitly assume some spirit of patriotic mutual sacrifice on the part of voters: you might be the loser on this occasion, but next time you might be the beneficiary.

Why then do the events of the past year feel quite so damaging to the ideal of quantitative expertise and its role in political debate?

Statistics, collected and compiled by technical experts, are giving way to data that accumulates by default, as a consequence of sweeping digitisation. Traditionally, statisticians have known which questions they wanted to ask regarding which population, then set out to answer them.

In this new world, data is captured first and research questions come later.

First, there is no fixed scale of analysis (such as the nation) nor any settled categories (such as unemployed).

Second, the majority of us are entirely oblivious to what all this data says about us, either individually or collectively.

What is most politically significant about this shift from a logic of statistics to one of data is how comfortably it sits with the rise of populism.

These data analysts are often physicists or mathematicians, whose skills are not developed for the study of society at all.

During the presidential election campaign, Cambridge Analytica drew on various data sources to develop psychological profiles of millions of Americans, which it then used to help Trump target voters with tailored messaging.

The new apparatus of number-crunching is well suited to detecting trends, sensing the mood and spotting things as they bubble up. It serves campaign managers and marketers very well. It is less well suited to making the kinds of unambiguous, objective, potentially consensus-forming claims about society that statisticians and economists are paid for.
statistics  expert  data  crisis  opinion  conflict 
january 2017 by aries1988
怎样快速入门考研高频词汇?
AntConc 是什么?

AntConc是一款强大的绿色工具软件,由日本学者Laurence Anthony开发,具有词语检索、统计词频和生成词表等功能。使用AntConc可以很方便地统计出英文文本中的词频,并且按照单词在文本中出现的频率高低进行排列,而且还可以将统计后的结果导出。
howto  statistics  tool  word  vocabulary 
december 2016 by aries1988
The Odds, Continually Updated - NYTimes.com
By contrast, Bayesian calculations go straight for the probability of the hypothesis, factoring in not just the data from the coin-toss experiment but any other relevant information — including whether you’ve previously seen your friend use a weighted coin.

Scientists who have learned Bayesian statistics often marvel that it propels them through a different kind of scientific reasoning than they’d experienced using classical methods.

“Statistics sounds like this dry, technical subject, but it draws on deep philosophical debates about the nature of reality,” said the Princeton University astrophysicist Edwin Turner, who has witnessed a widespread conversion to Bayesian thinking in his field over the last 15 years.

One downside of Bayesian statistics is that it requires prior information — and often scientists need to start with a guess or estimate. Assigning numbers to subjective judgments is “like fingernails on a chalkboard,” said physicist Kyle Cranmer, who helped develop a frequentist technique to identify the latest new subatomic particle — the Higgs boson.

A famously counterintuitive puzzle that lends itself to a Bayesian approach is the Monty Hall problem, in which Mr. Hall, longtime host of the game show “Let’s Make a Deal,” hides a car behind one of three doors and a goat behind each of the other two. The contestant picks Door No. 1, but before opening it, Mr. Hall opens Door No. 2 to reveal a goat. Should the contestant stick with No. 1 or switch to No. 3, or does it matter?

A Bayesian calculation would start with one-third odds that any given door hides the car, then update that knowledge with the new data: Door No. 2 had a goat. The odds that the contestant guessed right — that the car is behind No. 1 — remain one in three. Thus, the odds that she guessed wrong are two in three. And if she guessed wrong, the car must be behind Door No. 3. So she should indeed switch.
explained  statistics  science  scientist  today 
october 2014 by aries1988

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