题目
Statistics are persuasive． So much so that people， organizations， and whole countries base some of their most important decisions on organized data． But there's a problem with that． Any set of statistics might have something lurking inside it， something that can turn the results completely upside down．For example， imagine you need to choose between two hospitals for an elderly relative's surgery． Out of each hospital's last 1000 patients， 900 survived at Hospital A， while only 800 survived at Hospital B． So it looks like Hospital A is the better choice． But before you make your decision， remember that not all patients arrive at the hospital with the same level of health． And if we divide each hospital's last 1000 patients into those who arrived in good health and those who arrived in poor health， the picture starts to look very different．Hospital A had only 100 patients who arrived in poor health， of which 30 survived． But Hospital B had 400， and they were able to save 210． So Hospital B is the better choice for patients who arrive at hospital in poor health， with a survival rate of 52.5%． And what if your relative's health is good when she arrives at the hospital？ Strangely enough， Hospital B is still the better choice， with a survival rate of over 98%．So how can Hospital A have a better overall survival rate if Hospital B has better survival rates for patients in each of the two groups？ What we've stumbled upon is a case of Simpson's paradox， where the same set of data can appear to show opposite trends depending on how it's grouped．This often occurs when collected data hides a conditional variable， sometimes known as a lurking variable， which is a hidden additional factor that significantly influences results． Here， the hidden factor is the relative proportion of patients who arrive in good or poor health．Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts． One study in the UK appeared to show that smokers had a higher survival rate than nonsmokers over a twenty-year time period． That is， until dividing the participants by age group showed that the nonsmokers were significantly older on average， and thus， more likely to die during the trial period， precisely because they were living longer in general． Here， the age groups are the lurking variable， and are vital to correctly interpret the data．So how do we avoid falling for the paradox？ Unfortunately， there's no one-size-fits-all answer． Data can be grouped and divided in any number of ways， and overall numbers may sometimes give a more accurate picture than data divided into misleading or arbitrary categories． All we can do is carefully study the actual situations the statistics describe and consider whether lurking variables may be present． Otherwise， we leave ourselves vulnerable to those who would use data to manipulate others and promote their own agendas．47． What is meant to be conveyed by the hospital example？ ____ A．Statistics are persuasive．B．Statistics can be misleading．C．Statistics should be the basis of decisions．D．Statistics always have something hidden behind．48． What does the underlined word "hypothetical" in paragraph 6 mean？ ____ A．imaginaryB． actualC． obviousD． typical49． What can be inferred from the passage？ ____ A．One cannot avoid falling for Simpson's paradox．B．A lurking variable is different from a conditional variable．C．Simpson's paradox is named after a statist named Simpson．D．The way a set of data is grouped will influence the trend it shows．50．What does the writer suggest in decision making？ ____ A．Find a one-size-fits-all solution to the Simpson Paradox．B．Cooperate with data-manipulators as possible as you can．C．Study the statistics carefully and check the lurking variable．D．Focus on the overall numbers since it gives a more accurate picture．
Statistics are persuasive． So much so that people， organizations， and whole countries base some of their most important decisions on organized data． But there's a problem with that． Any set of statistics might have something lurking inside it， something that can turn the results completely upside down．
For example， imagine you need to choose between two hospitals for an elderly relative's surgery． Out of each hospital's last 1000 patients， 900 survived at Hospital A， while only 800 survived at Hospital B． So it looks like Hospital A is the better choice． But before you make your decision， remember that not all patients arrive at the hospital with the same level of health． And if we divide each hospital's last 1000 patients into those who arrived in good health and those who arrived in poor health， the picture starts to look very different．
Hospital A had only 100 patients who arrived in poor health， of which 30 survived． But Hospital B had 400， and they were able to save 210． So Hospital B is the better choice for patients who arrive at hospital in poor health， with a survival rate of 52.5%． And what if your relative's health is good when she arrives at the hospital？ Strangely enough， Hospital B is still the better choice， with a survival rate of over 98%．
So how can Hospital A have a better overall survival rate if Hospital B has better survival rates for patients in each of the two groups？ What we've stumbled upon is a case of Simpson's paradox， where the same set of data can appear to show opposite trends depending on how it's grouped．
This often occurs when collected data hides a conditional variable， sometimes known as a lurking variable， which is a hidden additional factor that significantly influences results． Here， the hidden factor is the relative proportion of patients who arrive in good or poor health．
Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts． One study in the UK appeared to show that smokers had a higher survival rate than nonsmokers over a twenty-year time period． That is， until dividing the participants by age group showed that the nonsmokers were significantly older on average， and thus， more likely to die during the trial period， precisely because they were living longer in general． Here， the age groups are the lurking variable， and are vital to correctly interpret the data．
So how do we avoid falling for the paradox？ Unfortunately， there's no one-size-fits-all answer． Data can be grouped and divided in any number of ways， and overall numbers may sometimes give a more accurate picture than data divided into misleading or arbitrary categories． All we can do is carefully study the actual situations the statistics describe and consider whether lurking variables may be present． Otherwise， we leave ourselves vulnerable to those who would use data to manipulate others and promote their own agendas．
47． What is meant to be conveyed by the hospital example？ ____
A．Statistics are persuasive．
B．Statistics can be misleading．
C．Statistics should be the basis of decisions．
D．Statistics always have something hidden behind．
48． What does the underlined word "hypothetical" in paragraph 6 mean？ ____
A．imaginary
B． actual
C． obvious
D． typical
49． What can be inferred from the passage？ ____
A．One cannot avoid falling for Simpson's paradox．
B．A lurking variable is different from a conditional variable．
C．Simpson's paradox is named after a statist named Simpson．
D．The way a set of data is grouped will influence the trend it shows．
50．What does the writer suggest in decision making？ ____
A．Find a one-size-fits-all solution to the Simpson Paradox．
B．Cooperate with data-manipulators as possible as you can．
C．Study the statistics carefully and check the lurking variable．
D．Focus on the overall numbers since it gives a more accurate picture．
For example， imagine you need to choose between two hospitals for an elderly relative's surgery． Out of each hospital's last 1000 patients， 900 survived at Hospital A， while only 800 survived at Hospital B． So it looks like Hospital A is the better choice． But before you make your decision， remember that not all patients arrive at the hospital with the same level of health． And if we divide each hospital's last 1000 patients into those who arrived in good health and those who arrived in poor health， the picture starts to look very different．
Hospital A had only 100 patients who arrived in poor health， of which 30 survived． But Hospital B had 400， and they were able to save 210． So Hospital B is the better choice for patients who arrive at hospital in poor health， with a survival rate of 52.5%． And what if your relative's health is good when she arrives at the hospital？ Strangely enough， Hospital B is still the better choice， with a survival rate of over 98%．
So how can Hospital A have a better overall survival rate if Hospital B has better survival rates for patients in each of the two groups？ What we've stumbled upon is a case of Simpson's paradox， where the same set of data can appear to show opposite trends depending on how it's grouped．
This often occurs when collected data hides a conditional variable， sometimes known as a lurking variable， which is a hidden additional factor that significantly influences results． Here， the hidden factor is the relative proportion of patients who arrive in good or poor health．
Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts． One study in the UK appeared to show that smokers had a higher survival rate than nonsmokers over a twenty-year time period． That is， until dividing the participants by age group showed that the nonsmokers were significantly older on average， and thus， more likely to die during the trial period， precisely because they were living longer in general． Here， the age groups are the lurking variable， and are vital to correctly interpret the data．
So how do we avoid falling for the paradox？ Unfortunately， there's no one-size-fits-all answer． Data can be grouped and divided in any number of ways， and overall numbers may sometimes give a more accurate picture than data divided into misleading or arbitrary categories． All we can do is carefully study the actual situations the statistics describe and consider whether lurking variables may be present． Otherwise， we leave ourselves vulnerable to those who would use data to manipulate others and promote their own agendas．
47． What is meant to be conveyed by the hospital example？ ____
A．Statistics are persuasive．
B．Statistics can be misleading．
C．Statistics should be the basis of decisions．
D．Statistics always have something hidden behind．
48． What does the underlined word "hypothetical" in paragraph 6 mean？ ____
A．imaginary
B． actual
C． obvious
D． typical
49． What can be inferred from the passage？ ____
A．One cannot avoid falling for Simpson's paradox．
B．A lurking variable is different from a conditional variable．
C．Simpson's paradox is named after a statist named Simpson．
D．The way a set of data is grouped will influence the trend it shows．
50．What does the writer suggest in decision making？ ____
A．Find a one-size-fits-all solution to the Simpson Paradox．
B．Cooperate with data-manipulators as possible as you can．
C．Study the statistics carefully and check the lurking variable．
D．Focus on the overall numbers since it gives a more accurate picture．
题目解答
答案
47．B．细节理解题．根据第一段最后一句Any set of statistics might have something lurking inside it， something that can turn the results completely upside down．任何统计数据都可能隐藏着某种东西，某种东西可以改变我们的看法．结果完全颠倒了．可知下文的举例是为了说明前文的观点，故选B．
48．A．词义猜测．根据倒数第二段Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts．simpson的悖论不仅仅是一种假设的情况，它在现实世界中不时出现，有时在重要的背景下出现，可知划线部分意思是假设含义，故选A．
49．B．推理判断题．根据第四，第五段This often occurs when collected data hides a conditional variable， sometimes known as a lurking variable， which is a hidden additional factor that significantly influences results． Here， the hidden factor is the relative proportion of patients who arrive in good or poor health．Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts．通常发生在收集到的数据隐藏了一个条件变量，有时被称为潜伏变量，这是一个隐藏的额外因素，对结果有重大影响．simpson的悖论不仅仅是一种假设的情况，它在现实世界中不时出现，有时在重要的背景下出现．可知潜伏变量与条件变量不同，故选B．
50．C．推理判断题．根据最后一段 Data can be grouped and divided in any number of ways， and overall numbers may sometimes give a more accurate picture than data divided into misleading or arbitrary categories． All we can do is carefully study the actual situations the statistics describe and consider whether lurking variables may be present． Otherwise， we leave ourselves vulnerable to those who would use data to manipulate others and promote their own agendas．数据可以以各种方式分组和划分，总的数字有时会比划分为误导或任意类别的数据更准确．统计数字描述并考虑潜在的变量是否存在．否则，我们容易受到那些利用数据来操纵他人和推动自己议程的人的伤害．可知作者建议仔细研究统计数据，检查潜在变量．故选C．
48．A．词义猜测．根据倒数第二段Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts．simpson的悖论不仅仅是一种假设的情况，它在现实世界中不时出现，有时在重要的背景下出现，可知划线部分意思是假设含义，故选A．
49．B．推理判断题．根据第四，第五段This often occurs when collected data hides a conditional variable， sometimes known as a lurking variable， which is a hidden additional factor that significantly influences results． Here， the hidden factor is the relative proportion of patients who arrive in good or poor health．Simpson's paradox isn't just ahypothetical situation． It pops up from time to time in the real world， sometimes in important contexts．通常发生在收集到的数据隐藏了一个条件变量，有时被称为潜伏变量，这是一个隐藏的额外因素，对结果有重大影响．simpson的悖论不仅仅是一种假设的情况，它在现实世界中不时出现，有时在重要的背景下出现．可知潜伏变量与条件变量不同，故选B．
50．C．推理判断题．根据最后一段 Data can be grouped and divided in any number of ways， and overall numbers may sometimes give a more accurate picture than data divided into misleading or arbitrary categories． All we can do is carefully study the actual situations the statistics describe and consider whether lurking variables may be present． Otherwise， we leave ourselves vulnerable to those who would use data to manipulate others and promote their own agendas．数据可以以各种方式分组和划分，总的数字有时会比划分为误导或任意类别的数据更准确．统计数字描述并考虑潜在的变量是否存在．否则，我们容易受到那些利用数据来操纵他人和推动自己议程的人的伤害．可知作者建议仔细研究统计数据，检查潜在变量．故选C．
解析
步骤 1：理解问题背景
文章通过医院的例子说明统计数据可能具有误导性，因为数据中可能隐藏着影响结果的条件变量。文章还提到Simpson悖论，即同一组数据根据分组方式不同，可能显示出相反的趋势。
步骤 2：分析问题
问题47要求理解医院例子所传达的信息。问题48要求理解“hypothetical”一词的含义。问题49要求从文章中推断出的信息。问题50要求理解作者在决策中建议的内容。
步骤 3：回答问题
根据文章内容，医院例子说明统计数据可能具有误导性，因此选项B是正确的。根据上下文，“hypothetical”一词的含义是“假设的”，因此选项A是正确的。根据文章内容，可以推断出潜伏变量与条件变量不同，因此选项B是正确的。根据文章内容，作者建议在决策中仔细研究统计数据并检查潜伏变量，因此选项C是正确的。
文章通过医院的例子说明统计数据可能具有误导性，因为数据中可能隐藏着影响结果的条件变量。文章还提到Simpson悖论，即同一组数据根据分组方式不同，可能显示出相反的趋势。
步骤 2：分析问题
问题47要求理解医院例子所传达的信息。问题48要求理解“hypothetical”一词的含义。问题49要求从文章中推断出的信息。问题50要求理解作者在决策中建议的内容。
步骤 3：回答问题
根据文章内容，医院例子说明统计数据可能具有误导性，因此选项B是正确的。根据上下文，“hypothetical”一词的含义是“假设的”，因此选项A是正确的。根据文章内容，可以推断出潜伏变量与条件变量不同，因此选项B是正确的。根据文章内容，作者建议在决策中仔细研究统计数据并检查潜伏变量，因此选项C是正确的。