| 김일중 2019-04-12 | ||
아래는 상여 나갈 때 제가 어릴 때 듣던 상여가입니다. 상여 나갈 때는 만장이 바람에 나부끼고, 갈까마귀들도 상여를 앞 서서 울고 갔지요. 사람들은 그 노래를 듣고 울었지요. 조 회 장님의 타계를 두고 슬퍼하는 이들이 많은 것을 보면, 그분은 선한 삶을 살다 가셨습니다. “어하넘 어하넘 명정공포 우뇌상에 요령소리 한심하다. 멀고 먼 황천길을 인지가면 언제오리 어하넘 어하넘 어나라 남천 어하넘 이 길을 인지 가면 언제 다시 돌아오리 활창겉이 굽은 길을 살대겉이 내가 가네. 북망산천 들어가서 띠잔디를 이불 삼고 쉬포리를 벗을 삼고 가랑비 굵은 비는 시우 섞어 오시는데 어는 구가 날 찾으리 어하넘 어하넘“ (조갑제닷컴 댓글) --------------------------------------------------------------------------  
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어제 동대문 근처 역사문제연구소에 모여들었다는 자칭 한국사 연구자들은
김일성을 추종하는 NL파들의 지도를 받으며 대학에서 밥 처먹고 있는 좌빨들인데
학문적 진실 따위는 개에게나 주고 공산주의에 대한 그들의 이념을 설파하는 선동원이라고 보면 된다.
그들은 애국우파가 만들고 일군 이 나라 대한민국의 정통성을 부정하는 것을 넘어
그 자들이 꿈꾸는 좌익 이념이 한반도 전역에 실현되는 경지를 향해 모여든다.
그것들은 지금까지 현대사의 정통은 이승만이 아니라 반미세력에게 있다고 설파하는데
그 반미의 챔피언이 김일성이고 김일성을 수령으로 모시는 적화한반도가 지상천국이라는
환상 속에 서로서로 대가리 속에 좌빨뽕을 주사하며 딸딸이나 치면서 상황을 지켜보다가
이제 부칸의 멸망이 가시화되자 자신들의 이념 수정이 필요하다는 데 동의한 것으로 보인다.
그들의 역사연구를 규정하는 것은 좌익이념이다.
그 이념의 지향점이 사회주의 헤게머니 유지와 공산주의 실현에 있음은 이 바닥에서 다 알고있다.
그 지향점이 달라졌음이 어제 동대문 회합에서 드러났다.
일왈, 김일성 노선에서 박헌영 노선으로 갈아타는 것이다.
이제부터 그들은 박헌영을 이승만을 대체하는 적화한반도의 시조로 떠받들고자 광분할 것이다.
[출처] 어제 동대문에 집결한 역사학자라는 좌빨들의 새로운 노선 ------------------------------------------------------------------------------- 
김정은이 핵을 포기하게 하는 유일한 방법은, 그에게 다른 선
택권을 주지 않는 것이다.
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북한의 비핵화를 위해 한국까지도 제재해야 한다.
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중산층의 상대성 이론
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"In poor countries, officials receive explicit bribes; in D.C. they
get the sophisticated, implicit, unspoken promise to work for
large corporations" -
미국에서는 은밀하고 교묘하게, 대기업에서 일하게 해주겠다 는 무언의 약속을 해준다.
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 표현했다. 이는 남성이 햇빛에 더 많이 노출되었기 때문이다. 19세기 다마스커스에서는 흰 피부를 유지하고 노화를 피하 기 위해 여성은 절대 햇빛을 보지 않게 했다. | ||
탈레브는 중요한 것은 "옳은" 것이 아니라 생존하는 것이며, 우리가 예측할 수 없고, 또 대체적으로 이해하지 못하는 세상에서, 이익을 얻는 것이라고 말한다.
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좌파 정치가들의 최대의 착각은 트윗의 인기를 유권자들 사이의 인기라고 착각하는 것이다.
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서구 전체에 독립적인 사상가들을 탄압하는 풍조가 퍼져가고 있다. 그렇게 되면 우리의 대학은 지식의 전당이 아니라 교회가 된다.
크리스티나 소머즈: 멍청이 경계령!
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좋은 통계와 나쁜 통계(또는 통계를 사람을 속이는 방법)
카드와 크루거의 최저 임금에 대한 연구는 최저 임금 인상이 실업을 증가시킨다는 결과를 뒤엎었다고 했지만, 그들은 최저 임금이 오른 이후 살아남은 사람들만을 상대로 조사를 했기 때문에, 그로 인해 망했거나 실업자가 된 사람들은 무시되었다.
이는 말하자면 러시아 룰렛 게임을 조사하면서 살아남은 사람들만을 상대로 조사하면, 러시아 룰렛 게임에서 죽음 사람은 없다는 황당한 결론이 나오는 것과 유사하다.
Good Statistics, and Bad
Gary Galles
Suppose someone wanted to misrepresent a public policy to you. How could they do so most effectively? And who can help you resist?
It’s certainly a believable hypothetical. With two major parties who seem to disagree on everything, multiple intra-party fault-lines, and a plethora of interests who wish to turn laws and regulations in their favor, whipped together by a press in search of partisan scandal and ratings, it is hard to see how it could be otherwise. In fact, for almost every issue, it seems very likely that some, if not many, groups, will be tempted to promote their interests using techniques ranging along the spectrum from “putting one’s best foot forward” to bald-faced lies.
There are plenty of common political tricks that fall short of outright lying. For instance, one can bury desired changes in the paper avalanche of an omnibus bill, as in the Minnesota legislature’s recent attempt to sneak in enactment of the National Popular Vote project. Or one can pass vague legislation that passes the buck for what it will mean in practice to executive agencies and the courts. But such forms of subterfuge are not my interest here.
I wish to ask how people would misrepresent things in the open, rather than behind such political camouflage? As I warn my public policy students, the general principle is that people will lie to you in whatever areas you are most vulnerable.
If you are American, one of those weak spots is typically mathematics, and particularly statistics, which is why it earns its place of shame along with lies and damned lies. That is why the tricks for how to misrepresent statistics discussed in Darrell Huff’s How to Lie with Statistics still keep the book selling 65 years after its initial publication.
However, widespread ignorance goes deeper than the science of statistics itself. Very few people have a clear idea on what the data involved actually measures, under what assumptions and limitations, which can lead to careless and irresponsible usage. For instance, few people can articulate why both the employment and unemployment rates could go up at the same time, and which would be a more reliable economic indicator in such a case, when their names suggest it shouldn’t be possible.
Thomas Sowell , in his most recent book, Discrimination and Disparities, describes the problem as “overlooking simple but fundamental questions as to whether the numbers on which… analyses are based are in fact measuring what they seem to be measuring, or claim to be measuring,” which, in order to defend ourselves against misrepresentation, requires “much closer scrutiny at a fundamental level.” But far too few apply such careful, fundamental scrutiny.
However, there are a few people who do yeoman work in this area, providing valuable “insurance” against errors others would encourage us to make. They deserve our appreciation for toiling in that underserved area, and I would like to express thanks to several whose efforts I have particularly benefitted from.
Thomas Sowell is one such author who has provided a great deal of clarification over decades of prolific publication. For example, one common theme of his is the need to distinguish between what happens to a particular category of people (e.g., “the rich” or “the poor”), interpreted as a stable group, which lends itself to class-based conclusions, and the very different experiences of real people who move in an out of such categories over time, which upsets such analyses.
Discrimination and Disparities reiterates that theme from his earlier books. But my favorite illustration is his discussion of the famous Card and Krueger minimum wage study, which purported to overturn the conclusion that raising the minimum wage increases unemployment. It surveyed the same employers, asking how many employees they had before and after a minimum wage increase. The problem is that “you can only survey the survivors.” Anyone who went out of business, and the jobs that consequently disappeared, would not be included, so even if surveyed survivors did not reduce employment, many jobs invisible to their approach could still have been lost. To reinforce the image, he notes that a similar before-and-after survey of those who played Russian Roulette would show that no one was hurt, and cites a quip by George Stigler that if it had been used in a survey of American veterans in both 1940 and 1946, it would “prove” that “no solider was mortally wounded” during the war.
Another very prolific watchdog for statistical malfeasance is Mark J. Perry . He points out so many useful “red flags” in multiple outlets that I look forward to what is almost a one-a-day pleasure. A good example is his evisceration of “Equal Pay Day” discussions that attribute differences between median yearly incomes to unjustifiable discrimination against women “doing the same work as men.” He points out that the data fails to adjust for differences in “hours worked, marital status, number of children, education, occupation, number of years of continuous uninterrupted job experience, working conditions, work safety, workplace flexibility, family friendliness of the workplace, job security, and time spent commuting,” each of which would lead men to be paid more, on average.
Andrew Biggs is another stickler for statistical responsibility, particularly in areas connected to retirement security and retirement plans. For instance, in Forbes , he showed that a recent GAO report concluding that 48% of U.S. households aged 55 and over in 2016 “had no retirement savings” was far different from reality, as 72% of people had such savings plan, when those with traditional defined benefit pensions are counted, and 83% of married households had such savings when including those where only one had a retirement plan. Just those two changes massively changed the conclusions. And he pointed out other biases, as well.
These three people have each helped me understand measurement issues far better than before, enabling me to avoid errors that would have undermined my analyses of policy issues. I owe them thanks. But readers might also give them more attention, for similar “tutoring.” Many others have also been of use to me, and as I continue to learn, perhaps I can give a shout-out to others in the future, especially as this labor pool is still far too shallow. But mainly I wanted to put out a serious warning about ignorance not only of statistical applications and presentations, but also of the data that is often misused in reaching policy conclusions.
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