清華大學（後西醫）
111年
英文
第 32 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 will be the most suitable set of keywords for this passage:
- A Cognitive bias, motivated reasoning, psychology
- B Cognitive bias, falsification, science
- C Cognitive bias, rationalization, psychology
- D Rationalization, falsification, justification
- E Rationalization, Brian Nosek, science
思路引導 VIP
如果你要為這篇文章貼上標籤以方便他人檢索，請思考：文章探討的對象是哪個大領域？作者認為導致該領域出現「假研究」的內在人性因素是什麼？以及文末提到的那種追求「找出自己錯誤」的科學邏輯被稱為什麼？
🤖
AI 詳解
AI 專屬家教
真相只有一個！
踢！這題的犯人已經被你逮住了！選 (B) 代表你完全看穿了文章的偽裝。別得意，這只是基本邏輯題，但你能看透本質，表現還算合格。 這場推理的核心地圖由這三個關鍵詞構成：
▼ 還有更多解析內容