In a ten-year study, one group of volunteers was given a medical

Question

﻿In a ten-year study, one group of volunteers was given a medical screening for disease X every year, whereas an otherwise similar group of the same size was only screened for disease X at the end of the study. Nine percent of the first group were diagnosed with disease X during the study and received treatment, but only six percent of the second group were diagnosed with disease X when they received the screening at the end of the study. The researchers concluded that during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group.

In order to evaluate the strength of the researcher's reasoning, it would be most helpful to know which of the following?

A. Whether there were statistically significant lifestyle differences between the individuals who were diagnosed with disease X and those who were not

B. How many people volunteered for the study because they knew that they had an especially high risk for disease X

C. How long it takes to be treated for disease X

D. Whether volunteers were told what disease they were being screened for

E. How frequently on average the medical screening used in the study produces erroneous diagnoses of disease X

MartyMurray · Accepted Answer

Here's my take:

In a ten-year study, one group of volunteers was given a medical screening for disease X every year, whereas an otherwise similar group of the same size was only screened for disease X at the end of the study. Nine percent of the first group were diagnosed with disease X during the study and received treatment, but only six percent of the second group were diagnosed with disease X when they received the screening at the end of the study. The researchers concluded that during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group.

In order to evaluate the strength of the researcher's reasoning, it would be most helpful to know which of the following?

The researchers' conclusion is the following:

during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group

The researchers' reasoning is essentially the following:

Since nine percent of the volunteers in the screened group were diagnosed with disease X during the ten-year period of the study, it stands to reason that around nine percent of the group that was screened only at the end of the study also experienced disease X during the ten-year period of the study. Thus, the reason why only six percent of the volunteers in the second group were diagnosed with disease X at the end of the study must be that disease X did appear in some other individuals in the second group but disappeared without treatment.

The correct answer will help with evaluating whether the conclusion follows from the researchers' reasoning.

A. Whether there were statistically significant lifestyle differences between the individuals who were diagnosed with disease X and those who were not

This choice uses the term "statistically significant" to seem important, but we can shorten this choice to "whether there were ... lifestyle differences between the individuals who were diagnosed with disease X and those who were not."

Now, what we need to notice to see why the answer to the question posed by this choice doesn't affect the argument is the following. This choice about differences between people diagnosed with disease X and people not diagnosed with disease X whereas the argument is about a difference between the rates of diagnosis in two groups of people, in each of which some members were diagnosed with disease X and others were not.

Put simply, this choice and the argument are about two different differences. So, regardless of what the answer to this question is, that answer doesn't affect the argument.

Eliminate.

B. How many people volunteered for the study because they knew that they had an especially high risk for disease X

Regardless of how many people volunteered for or participated in the study because they had an especially high risk for disease X, that information would not give us any insight into why there was a difference between the percentages of the volunteers in the two groups who experienced disease X. After all, that information would not indicate that there was any difference between the risks for disease X of the members of the two groups.

In fact, the passage tells us that, other than in the way the volunteers in the two groups were screened for disease X, the two groups were "similar." So, presumably, the risk for disease X of the volunteers in the two groups was, on average, similar.

So, the information mentioned by this choice would help us to understand only the risk for disease X of all the volunteers in the two groups, not any difference between the two groups.

Eliminate.

C. How long it takes to be treated for disease X

Information on how long it takes to be  treated  for disease X does not help us to understand why there was a difference between the percentages of the members of the two groups who were  diagnosed  with disease X. After all, regardless of how long it takes to be treated for disease X, it remains the case that more people in the first group than in the second group were diagnosed with disease X, and the volunteers in both groups had the same amount of time to develop disease X.

So, regardless of how long it takes to be treated for disease X, we still have basically the same information about people being diagnosed with disease X we had before.

Eliminate.

D. Whether volunteers were told what disease they were being screened for

Regardless of whether the volunteers were told what disease they were being screened for, they were still diagnosed with disease X in different percentages in the two groups, and this choice does not help us to determine why that difference existed.

After all, even if there's some way telling the volunteers would have affected whether they experienced disease X, this choice does not say that the volunteers in one group but not the other were told what disease they were being screened for.

So, regardless of whether, yes, this choice is true or, no, this choice is not true, the support the evidence we have provides for the conclusion is the same.

Eliminate.

E. How frequently on average the medical screening used in the study produces erroneous diagnoses of disease X

The information mentioned by this choice would help us to evaluate the case for believing that disease X must have disappeared without medical treatment in some individuals in the second group.

After all, if the answer is that the medical screening used in the study NEVER produces erroneous diagnoses of disease X, then we have eliminated a possible alternative cause for the difference between the percentages of the volunteers in the two groups diagnosed with disease X. We've eliminated the possibility that the difference is attributable to erroneous diagnoses. So, information that the frequency of erroneous diagnoses is never strengthens the case for the researchers' conclusion.

On the other hand, if the medical screening used in the study OFTEN produces erroneous diagnoses of disease X, then we have a possible alternative cause for the difference between the percentages of the volunteers in the two groups diagnosed with disease X. It could be that the reason for the difference is that some of the diagnoses of the volunteers in the first group were erroneous.

In fact, notice that, if the medical screening often produces erroneous diagnoses of disease X, then the fact that the members of the first group were screened more gave them more opportunities to be erroneously diagnosed with disease X.

So, the information that the frequency of erroneous diagnoses is often weakens the case for the researchers' conclusion by providing reason to believe that there may have been a different cause for the difference between the percentages of the volunteers in the two groups diagnosed with disease X.

So, having information on how frequently the medical screening produces erroneous diagnoses of disease X would help us to strengthen or weaken the case for the researchers' conclusion and thus evaluate the argument.

The correct answer is (E).

AndrewN · Answer

In the interest of helping the community, I will offer my thoughts on the question. Tracing these lines of logic led me to the correct answer in 2:08.

In any evaluation CR question, the goal is to find an answer choice that creates a meaningful two-way conditional:

1) if Yes, then the argument or conclusion is affected in a significant way
2) if No, then the argument or conclusion is affected in a significant way

Note that it is  not  the goal to prove anything, or to create one meaningful conditional while ignoring the other. That said, we can break down the passage:

The opening line introduces a  ten-year study  and informs us on two groups in that study, one tested for a certain disease  every year , the other  at the end  of ten years.   The second line of the passage provides findings relating to each group:  nine percent  of the group screened annually  were diagnosed  with the disease, while  six percent  of the other group were diagnosed with the disease after a decade.   The conclusion appears to be drawn on a percent-to-percent comparison of the two groups, since  the researchers concluded that during the ten-year period... some individuals in the second group , which we can call the 6 percent group, must have had the disease but— without medical treatment —were no longer found to have it by the end. That is, the disease apparently  disappeared  on its own at some point during the decade.

I would not bother pre-thinking anything, but would jump straight into the answer choices with a firm grasp of just what the passage outlines and on what basis the conclusion is drawn.

I would start by pointing out that we have no idea what to make of  statistically significant . A mathematician friend of mine once busted me on this point when I had written a question that used such terminology. Researchers can set statistical significance at just about any level they choose, and track any variable they choose. Think about it: perhaps a 0.01 percent (1 in 10,000) incidence of a trait increases to 0.015 percent, representing a  fifty percent increase —something that sounds significant—yet the overall incidence remains well below even 1 percent. (Medical research uses this sleight of hand all the time, and irresponsible writers often spin sensationalist arguments without shedding light on the actual findings or figures.) In this case, what are  statistically significant lifestyle differences ? The difference between smoking and not smoking, or between the person who eats more than 6 servings of fruits and vegetables a day and one who eats 5? The term loses meaning once we stop and think about it.

The easier target lies at the end of the answer choice. The  individuals   not  diagnosed with the disease represent over 90 percent of the participants from each group. We are not examining the missing 3 percent between the 9 percent group and the 6 percent group. This consideration would not help to evaluate the argument.

It is hard to see how anything after  because  could make this consideration the one we need to evaluate the argument. Whether people  volunteered  for one reason or another, the results of the study would remain unchanged, and we would be no closer to gaining any insight into the difference between the groups.

You might argue that if the disease takes a few years to be treated, then the extra 3 percent in the 9 percent group might not have had time for the disease to be eradicated, hinging on another  if —if many of those diagnosed with the disease were found to have developed it more recently in the ten-year period. However, that still would not help explain why members of the other group, those who were  untreated  until the end of the ten-year period, did not develop the disease in like numbers. Focusing on the treatment period is a blind alley.

Even if you believe in psychosomatic relationships such that merely  thinking about  cancer could switch its development on or off, the fact remains that regardless of how you answer this question, yes or no, we would be no closer to teasing apart the members of the two groups. Presumably, they would all have been informed equally.

Now this is of interest. Remember, we have one group whose members are screened every year for a decade, and another group whose members get a single screening. You do not have to be a math guru to see that the balance is tilted 10:1. If we know this frequency, we can calibrate the results and make a more informed evaluation of the researchers' conclusion; if we do not, then the question remains why one group without any treatment seemed to come out ahead of the other group. This is a pertinent concern either way we look at it, and we have found our answer.

I would be remiss if I did not add that on more difficult questions, you are less likely to encounter easily discernible answer patterns. That is, I would  not  recommend memorizing this question and the correct answer choice in hopes of applying the same logic to the next evaluation question. The chain of logic and the contextual information are what matter. It is more important to establish a sound method by which to tackle such questions.

Good luck with your studies.

- Andrew

sayan640 · Answer

MartyMurray  , Would you like to discuss this question and explain why option C is incorrect and E is correct ?

anish777 · Answer

What if we consider this fact that the disease takes more than 10 years to be treated naturally. Then only 6% actually were diagnosed with the disease and they did not recovered in between, in contrast to what the conclusion suggests.
Can you help me decipher this one ?
 MartyMurray   GMATNinja

MartyMurray · Answer

Hi Anish.

As we've seen, the point of the argument is basically that there must be some reason why 9 percent of the screened people were diagnosed with the disease whereas only 6 percent of the non-screened people were.

Since the non-screened people were not treated over the ten-year period of the study, treatment could not be what made the difference between the percentage of those screened who were diagnosed and the percentage not screened who were diagnosed, even if treatment takes much less than 10 years.

So, the possibility that treatment takes more than 10 years is not relevant.

sachi-in · Answer

I was stuck between C and E and then made the mistake of choosing C too !

But after 1 week when I re-attempted this question I noticed a minute detail : Option C talks about  treatment  not time taken to  Heal .

Note the difference in the wordings in the question stem :

Treatment may take longer or shorter span of time. 
But if the disease disappears / heals in a longer or shorter time - only that makes the difference.

I guess gmat loves to trick answer choices using traps like this. Traps are more subtle for higher level problems.

ManavChawla · Answer

MartyMurray 
Would it be right to reject A because the passage mentions that the two groups were otherwise similar, so this choice sort of goes against what is mentioned in the passage?

sachi-in · Answer

For a faster analysis we can think of the following : 
1.  We need to find a logic that evaluates the reserarcher's reasoning  - diseases dissappeared by itself in group A ( not so in group B )

this logic would be something that will give us a reason to support one group over the other.

A. statistical difference pointed is not between group A and B but between people diagonsed with disease and not diagonsed
B. irrelevant ( doesn't support one group over the other )
C. irrelevant ( doesn't support one group over the other )
D. irrelevant ( doesn't support one group over the other )
 E. relevant ( points out difference between two groups why one group might be more likely to have less errors - because they have been tested multiple times during the past years ) by providing an alternative reasoning to the researcher's, it questions / evaluates wheather s/he is correct.

Gemmie · Answer

What the Researchers Are Saying:

The researchers believe disease X disappeared in some individuals in the second group (single screening) because they weren't diagnosed with it at the end of the study.

This implies the second group  initially had the disease  but somehow got rid of it  without treatment .

However, the conclusion relies on the assumption that all those not diagnosed in Group B were truly healthy.

The Missing Piece and Why Option (E) is Critical:

The key concern is the possibility of  false negatives  in Group B. These are individuals who actually had the disease but weren't diagnosed because of the single screening.

Option (E) addresses the frequency of false negatives  in the screening test used.

If the test has a high rate of false negatives, it could explain the lower diagnosis rate in Group B.

Missed diagnoses (false negatives) are a more likely explanation for the difference in diagnosed cases than the disease disappearing on its own.

A. Lifestyle Differences 
Option (A) focuses on a comparison within each group (diagnosed vs. not diagnosed), not between the groups themselves (annual vs. single screening).

B. Volunteer Risk Factors 
The researchers presented their findings as percentages (percent diagnosed in each group). Option (B) asks about the absolute number of volunteers who participated based on their risk awareness.

C. How long it takes to be treated for disease X. 
The focus is on whether those not diagnosed in Group B truly didn't have the disease or if the single screening missed it. Knowing the treatment duration wouldn't clarify this.

D. Whether volunteers were told what disease they were being screened for. 
While knowing the disease might influence volunteer behavior, it wouldn't change how well the test detects the disease.

Bhavita. · Answer

Team,

in option A why can't we say if there exists any significant differences between the people with and without disease then there is a possibility of this being a factor of difference between groups (in discussion of argument) and thus decreases the belief in the conclusion and likewise if there doesn't exist any significant differences it actually increases the belief in the conclusion since it rules out one such possible factor

SergejK · Answer

The argument works with many distractors, like when the results where measured with each group or that one group received treatment while leaving out if the second group received treatment. The trick to avoid overwhelming oneself is first look at the conclusion and what it is based on. Don't start planning how to use the information without knowing if there is actually a need. The passage presents us in a discrepancy between 2 data sets, that are otherwise SIMILAR. The argument then concludes that in the second group the disease disappeared without treatment. How likely is that? What are possible assumptions to arrive at that conclusion? Well we have 2 similar groups. If one of the groups knew what was the project about even if they were only tested after 10 years, maybe they could have taken actions to actually treat themselves So the the conclusion uses the assumption that this is not the case. Also, they researchers must assume that there is no mistake with the data itself. Both of these assumptions are needed to arrive at this conclusion, attacking one would suffice to weaken our belief. As this is an evaluate question, either a strengthener or weakener will suffice or an information will be provided or a question will be asked to help us better understand the validity of the conclusion. Choice E asks this very question, whether the measurement is reliable. A is out of scope as we don't know whether the lifestyles could lead to healing. It also attacks the premise as we are told that the groups were otherwise similar. B is almost correct, however, no mentioning of group 2, hence those who knew could actually be part of the first group. C is out of scope as we don't care for treatment but for the diagnosis. D looks as promising as B, however, equally as B it leaves out the connecting info about group 2.

siddharth_ · Answer

Hey  MartyMurray

Thanks for the elaborate solution.

Can you please help me with this - 
If choice C said - how long does it take for disease X to disappear?
Then could it help us in evaluating this argument?

Logic behind this-

1. If the time period for the disappearance of the disease is less than 10 years, then we can assume that both the groups had hundred participants each and in both the groups, nine people developed disease X but it might have disappeared for 3 people in the 6% group . So it strengthens the researchers’ reasoning.

2. But if the time period for disappearance of the disease is more than 10 years, then what the researchers are saying is weakened.

Now the doubt that I have is - even in the first scenario above, we have to make another assumption that the three people for whom the disease disappeared developed it in those 10 years, such that they had enough time left for disappearance. But at the same time, it gives us some reason to believe the researchers. so I’m not able to decide could we have still eliminated it?

billlyjoehbs · Answer

KarishmaB   MartyMurray   GMATNinja

I request your inputs in choosing between option C & E. I am not trying to debate the other experts' reasoning, but I am still not convinced with certain aspects.

in the question stem, "The researchers concluded that during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group"

C. How long it takes to be treated for disease X - Its given that those in group B were not treated during the study. But If i subject this option to the extremes test,

extreme 1 - It takes a really long time to treat the disease (lets say 20+ years) - Then those in group B, without receiving any treatment may not even have contacted the disease.

extreme 2 - it doesn't take long to treat the disease. Then those in group B can still have a chance of recovering without treatment.

I realize that the argument above is not air tight, but still can have some grounds in cases like Cancer

Now Option E- How frequently on average the medical screening used in the study produces erroneous diagnoses of disease X

Extreme 1 - Very frequently there are errors - Tells me that I cant rely on these studies. But doesn't still address the point regarding whether there are anyone in group b who contacted the disease and experienced its disappearance without treatment.

Extreme 2 - there are hardly any errors- Tells me that I can rely on these studies. But doesn't still address the point regarding whether there are anyone in group b who contacted the disease and experienced its disappearance without treatment, as it doesn't speak how long the person remains affected by the disease post contacting it.

From what we now, we have no clue about the possibility of contacting the disease in the first place. maybe those in group b are amongst those who didn't develop the disease. Just because I conduct tests every year for group A doesn't imply I can make conjectures about the prevalence of the disease across the population or the other group, expecially if group A is more prone to the disease.

Pls help,
Thanks in advance!

MartyMurray · Answer

It sounds as if you have confused "How long it takes to be treated for disease X" with how long it takes to recover from disease X.

The people in group B were not treated for disease X and treating people for disease X does not actually play any important role in the argument. So, how long treating disease X takes is completely irrelevant.

After all, if people in group B were not treated for the disease, then the time required for treatment has no bearing on how long they would have required to recover or whether they would have recovered from the disease.

It's true that we don't know for sure what happened regardless of how often the screening produces erroneous diagnoses, but the point is not to prove for sure whether the conclusion is correct but rather to evaluate the case for the conclusion, and knowing how frequently the screening produces erroneous diagnoses can help with that.

So, a key general takeaway is that the correct answer to a Weaken, Strengthen, or Evaluate question doesn't have to involve complete proof one way or the other. It has only to involve information that makes the case for the conclusion somewhat less or more convincing.

KarishmaB · Answer

Look at the big picture. A study with 2 grps - one tested every year and other tested only once after 10 years. 9% of first Grp diagnosed and treated. 6% of second group diagnosed at the end of 10 years. Conclusion of researchers : During the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group. Think about what the researchers are assuming to conclude that X MUST have disappeared in som people without treatment. they are assuming that in both groups, similar % of people must have contracted the disease (that is 9%). They are assuming that their tests do not have errors else the difference in the percentage could have been explained by testing errors. After assuming these, they are concluding that for some people the disease must have vanished on its own - 9% people contracted in both groups but since we did not test the second group, we never got to know and did not treat. So those people who go the disease say in 2nd year of study but who recovered on their own from it in 5th year of study never got counted in the second group. Whereas all such people got counted in the first group. The researchers are saying that this difference accounts for the diff of 3%. So what is helpful to know whether the researcher's reasoning makes sense? It is helpful to know whether the test is accurate. If it is not, then inaccuracies in the test could account for the 3% difference. That is why option (E) works. Look at the other options. A. Whether there were statistically significant lifestyle differences between the individuals who were diagnosed with disease X and those who were not The diff that we are interested in is group 1 and group 2. Why is there a 3% difference in instances of X in the two groups. Why people get X is irrelevant. So diff between those who get X and those who don't is irrelevant. B. How many people volunteered for the study because they knew that they had an especially high risk for disease X Irrelevant. Again, we need the diff between Grp 1 and 2. Were such people equally distributed among the two groups would be something we would be interested in. But how many total such people there were in the study is irrelevant. C. How long it takes to be treated for disease X Irrelevant. Treatment actually has nothing to do with this argument. The study simply tests one group every year and the other group once in 10 years. Overall they found 9% people got infected in the 1st group in the 10 years overall and 6% were found to be infected in group 2. Those who were found to be infected during these 10 years in group 1 were given treatment but even if they were not, it is irrelevant. They had already been counted as infected. They are a part of the 9%. Whether they got treatment or not doesn't impact the study numbers. They would NOT be counted again and again every year till the treatment is not complete. The argument says "nine percent of the first group..." it means that anyone counted is counted once only. D. Whether volunteers were told what disease they were being screened for Irrelevant. Answer (E) Here is another interesting discussion on useful to evaluate: https://youtu.be/oLKFSv9v8BA

MercedesF1 · Answer

KarishmaB  I chose option "C. How long it takes to be treated for disease X" because if the treatment time is less than 10 years, it means the disease could have appeared and been treated within the second group before the study ended. That would explain the observed discrepancy.

GMATQuizMaster · Answer

Hey, 
Let me try to help.

The conclusion says that the "disease disappeared without any medical treatment". Hence, the duration required to treat disease X is irrelevant.

You probably did not read/ understand the conclusion properly and hence got into this trap.

Good luck!

GMATQuizMaster · Answer

The trickiest part of this CR Evaluate question isn't the logic—it's avoiding assumptions.

Choices B and D seem tempting until you realize you're adding information that isn't there.

This video solution walks through why Choice E is the correct choice and includes an error analysis so you can pinpoint exactly where your thinking went off track:  https://www.youtube.com/watch?v=J8mL9K8lH8I

Hope this video will help you understand this question and the reason why you made a mistake.

Good luck!

Borrat · Answer

﻿In a ten-year study, one group of volunteers was given a medical screening for disease X every year, whereas an otherwise similar group of the same size was only screened for disease X at the end of the study. Nine percent of the first group were diagnosed with disease X during the study and received treatment, but only six percent of the second group were diagnosed with disease X when they received the screening at the end of the study. The researchers concluded that during the ten-year period, disease X must have disappeared without medical treatment in some individuals in the second group.

In order to evaluate the strength of the researcher's reasoning, it would be most helpful to know which of the following?

A. Whether there were statistically significant lifestyle differences between the individuals who were diagnosed with disease X and those who were not --  you would have to assume that lifestyle choices would change how the disease affects you -- too much speculation, this is inventing a chain of assumptions

B. How many people volunteered for the study because they knew that they had an especially high risk for disease X --  knowing the number doesn't differentiate between the 2 groups - it would just spread among the two groups.

C. How long it takes to be treated for disease X --  length is a frameshift

D. Whether volunteers were told what disease they were being screened for --  you would have to assume that knowing something before hand would alter your biology on how the disease affects you -- too much speculation, this is inventing a chain of assumptions

E. How frequently on average the medical screening used in the study produces erroneous diagnoses of disease X --  if the more tests then the more errors -artificial (due to machines) disappearance - this weakens the idea that   X disappeared naturally,

In a ten-year study, one group of volunteers was given a medical

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GMAT Critical Reasoning (CR) Questions

In a ten-year study, one group of volunteers was given a medical

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