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．