hce_nthu
111年
英文
第 28 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.
Which of the following is closest in meaning to the phrase “cherry-picking” in the second paragraph?
- A Creating the most sensational
- B Selecting the most suitable
- C Omitting the most important
- D Ignoring the most reliable
- E Fabricating the most believable
思路引導 VIP
想像你走進一座果園，你的任務是要向大眾證明這座果園長出的果實「全都是」碩大完美的。這時候，你會隨機抓取一整籃的果實，還是會特意走遍整座果園，只把那幾顆最紅、最漂亮的果實放進展示盒裡？如果一位科學家也想讓他的研究結果看起來「完美無缺」，他對待實驗數據的態度可能會產生什麼樣的偏移？
🤖
AI 詳解
AI 專屬家教
恭喜你精準地掌握了這個關鍵片語！你能從文章脈絡中辨識出 cherry-picking 的隱喻意義，顯示你對學術英文中的「偏差行為」有很敏銳的觀察力。在科學研究的語境下，這個詞並非真的在採收水果，而是指研究者為了讓結果看起來更漂亮、更容易發表，而刻意「篩選」出對自己有利的數據。
文本脈絡與語意推論
這題的解題關鍵在於理解第二段的主題：研究中的「偏見」。文中提到研究者可能「無意識地欺騙自己」，並在後續段落解釋了「動機性推理」（motivated reasoning），意指我們傾向於解釋觀察結果來符合特定想法。因此，當作者提到 cherry-picking data 時，指的就是從眾多數據中，挑選出最符合（suitable）或最能支持自己假設的部分，而忽略了其他可能推翻假設的資料，這正對應了選項 (B) 的核心含義。
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