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Posted on December 12, by Scott Alexander Aquinas famously said: For example, take medical research. Suppose a certain drug is weakly effective against a certain disease. After a few years, a bunch of different research groups have gotten their hands on it and done all sorts of different studies.
But there will also be random noise caused by inevitable variation and by some of the experiments being better quality than others. In the end, we might expect something looking kind of like a bell curve. We see that the peak of the curve is somewhere to the right of neutral — ie weakly effective — and that there are about 15 studies that find this correct result.
But there are also about 5 studies that find that the drug is very good, and 5 studies missing the sign entirely and finding that the drug is actively bad. This is before we get into fraud or statistical malpractice.
As we increase experimental rigor, the bell curve might get squashed horizontally, but there will still be a bell curve. But maybe the drug is more effective in bipolar i than in bipolar ii Depakote, for example Or maybe the drug is very effective against bipolar mania, but much less effective against bipolar depression Depakote again.
And the latest study shows that their drug is great! Or they could add all of those studies about slightly different things. Depakote is ineffective at treating bipolar depression.
Depakote is ineffective at maintenance bipolar therapy. Depakote is ineffective at bipolar ii. I think medical science is still pretty healthy, and that the consensus of doctors and researchers is more-or-less right on most controversial medical issues.
Like, take the minimum wage question please. We all know about the Krueger and Card study in New Jersey that found no evidence that high minimum wages hurt the economy. We probably also know the counterclaims that it was completely debunked as despicable dishonest statistical malpractice.
Maybe some of us know Card and Krueger wrote a pretty convincing rebuttal of those claims. Or that a bunch of large and methodologically advanced studies have come out since then, some finding no effect like Dubeothers finding strong effects like Rubinstein and Wither.
These are just examples; there are at least dozens and probably hundreds of studies on both sides. But we can solve this with meta-analyses and systemtic reviews, right?
Depends which one you want.
Do you go with this meta-analysis of fourteen studies that shows that any presumed negative effect of high minimum wages is likely publication bias? With this meta-analysis of sixty-four studies that finds the same thing and discovers no effect of minimum wage after correcting for the problem?
Or how about this meta-analysis of fifty-five countries that does find effects in most of them? Maybe you prefer this systematic review of a hundred or so studies that finds strong and consistent effects?
Can we trust news sources, think tanks, econblogs, and other institutions to sum up the state of the evidence? The weight of the evidence is that such impacts are small to none.
They seem like experts. What do they think?My son, who attended a Mass. public school, is now a college freshman, and I spent 10 years teaching freshman composition in universities.
I also have a child 7 years younger, so I have comparative experience with how public education has changed since Point size. Point size is perhaps the element most used to describe the legibility of a type face, but it can also be the most deceptive. Point size is a legacy from the letterpress system, where each letter is held on a small metal block.
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Introduction and Summary "Somewhere along the line of development we discover what we really are, and then we make our real decision for which we are responsible.
Aquinas famously said: beware the man of one book.I would add: beware the man of one study. For example, take medical research. Suppose a certain drug is weakly effective against a certain disease.
How to End an Essay. The final paragraph of an essay is what ties the piece together into a single, cohesive whole.
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