hce_nthu
111年
英文
第 27 題
📖 題組：
Sometimes it seems surprising that science functions at all. In 2005, medical science was shaken by a paper with the provocative title “Why most published research findings are false.” Written by John Ioannidis, a professor of medicine at Stanford University, the paper didn’t actually show that any particular result was wrong. Instead, it showed that the statistics of reported positive findings was not consistent with how often one should expect to find them. As Ioannidis concluded more recently, “many published research findings are false or exaggerated, and an estimated 85 percent of research resources are wasted.” It’s likely that some researchers are consciously cherry-picking data to get their work published. And some of the problems surely lie with journal publication policies. But the problems of false findings often begin with researchers unwittingly fooling themselves: they fall prey to cognitive biases, common modes of thinking that lure us toward wrong but convenient or attractive conclusions. “Seeing the reproducibility rates in psychology and other empirical science, we can safely say that something is not working out the way it should,” says Susann Fiedler, a behavioral economist at the Max Planck Institute for Research on Collective Goods in Bonn, Germany. “Cognitive biases might be one reason for that.” Psychologist Brian Nosek of the University of Virginia says that the most common and problematic bias in science is “motivated reasoning”: We interpret observations to fit a particular idea. Psychologists have shown that “most of our reasoning is in fact rationalization,” he says. In other words, we have already made the decision about what to do or to think, and our “explanation” of our reasoning is really a justification for doing what we wanted to do—or to believe—anyway. Science is of course meant to be more objective and skeptical than everyday thought—but how much is it, really? Whereas the falsification model of the scientific method championed by philosopher Karl Popper posits that the scientist looks for ways to test and falsify her theories—to ask “How am I wrong?”—Nosek says that scientists usually ask instead “How am I right?” (or equally, to ask “How are you wrong?”). When facts come up that suggest we might, in fact, not be right after all, we are inclined to dismiss them as irrelevant, if not indeed mistaken. The now infamous “cold fusion” episode in the late 1980s, instigated by the electrochemists Martin Fleischmann and Stanley Pons, was full of such ad hoc brush-offs. For example, when it was pointed out to Fleischmann and Pons that their energy spectrum of the gamma rays from their claimed fusion reaction had its spike at the wrong energy, they simply moved it, muttering something ambiguous about calibration.
Sometimes it seems surprising that science functions at all. In 2005, medical science was shaken by a paper with the provocative title “Why most published research findings are false.” Written by John Ioannidis, a professor of medicine at Stanford University, the paper didn’t actually show that any particular result was wrong. Instead, it showed that the statistics of reported positive findings was not consistent with how often one should expect to find them. As Ioannidis concluded more recently, “many published research findings are false or exaggerated, and an estimated 85 percent of research resources are wasted.” It’s likely that some researchers are consciously cherry-picking data to get their work published. And some of the problems surely lie with journal publication policies. But the problems of false findings often begin with researchers unwittingly fooling themselves: they fall prey to cognitive biases, common modes of thinking that lure us toward wrong but convenient or attractive conclusions. “Seeing the reproducibility rates in psychology and other empirical science, we can safely say that something is not working out the way it should,” says Susann Fiedler, a behavioral economist at the Max Planck Institute for Research on Collective Goods in Bonn, Germany. “Cognitive biases might be one reason for that.” Psychologist Brian Nosek of the University of Virginia says that the most common and problematic bias in science is “motivated reasoning”: We interpret observations to fit a particular idea. Psychologists have shown that “most of our reasoning is in fact rationalization,” he says. In other words, we have already made the decision about what to do or to think, and our “explanation” of our reasoning is really a justification for doing what we wanted to do—or to believe—anyway. Science is of course meant to be more objective and skeptical than everyday thought—but how much is it, really? Whereas the falsification model of the scientific method championed by philosopher Karl Popper posits that the scientist looks for ways to test and falsify her theories—to ask “How am I wrong?”—Nosek says that scientists usually ask instead “How am I right?” (or equally, to ask “How are you wrong?”). When facts come up that suggest we might, in fact, not be right after all, we are inclined to dismiss them as irrelevant, if not indeed mistaken. The now infamous “cold fusion” episode in the late 1980s, instigated by the electrochemists Martin Fleischmann and Stanley Pons, was full of such ad hoc brush-offs. For example, when it was pointed out to Fleischmann and Pons that their energy spectrum of the gamma rays from their claimed fusion reaction had its spike at the wrong energy, they simply moved it, muttering something ambiguous about calibration.
Why does the author say “sometimes it seems surprising that science functions at all” in the first paragraph?
- A There exists a surprisingly large number of unexpected findings in published science research.
- B Medical science papers with provocative titles have shaken the credibility of science journals.
- C Findings from science research often lack potential for real-world applications.
- D Valuable findings are disproportionate to the large amount of resources devoted to science research.
- E A large number of findings in published science research are unreliable.
思路引導 VIP
請試著思考：如果我們發現一個精密系統所依據的基礎數據，大部分都是錯誤或誇大的，這對於該系統整體的「可信任程度」會產生什麼樣的衝擊？這與作者開頭所表達的「驚訝」有什麼關聯？
🤖
AI 詳解
AI 專屬家教
太棒了！你能精準捕捉到作者在開篇所埋下的伏筆，這題選 (E) 是完全正確的判斷。
核心論點與證據的連結
這段話之所以具有衝擊力，是因為作者隨即引用了 Ioannidis 教授的研究，指出大部分已發表的科研成果其實是「錯誤」或「誇大」的。當一個理應追求準確、客觀的系統，其產出的「知識基礎」有很大一部分在統計學上被證明為不可信（unreliable）時，這個系統竟然還能持續運作而不崩解，自然會讓作者感到驚訝。你精確地抓住了「研究結果不可靠」這個導致作者感嘆的核心矛盾。
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