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Just an attempt:

The conclusion is: the programs are effective in teaching sound data-analysis practices. Proof? One-third of employed data analysts have completed such a program, only eight percent of analysts whose work resulted in serious reporting errors have done so, essentially translating to (without math): 1/3rd employees completed this program but less than 1/3rd serious reporting errors have occurred.

Between B and D:
B. Newly hired data analysts who are most likely to complete a training program are those who already have several years of experience working with data in related roles.
This proves that despite less reporting errors, the credit does not go to the program effectiveness. It's their own experience that helps.

D. Most serious reporting errors result from faulty data supplied to analysts rather than from mistakes made by the analysts themselves.
I thought this is the answer because if faulty data given to the analysts is the reason why reporting errors have occurred, then we know the they were already smart. Maybe they became smart after receiving the training but we still cant prove the effectiveness of the training because the evidence itself is faulty due to the faulty data supplied. So, If most serious reporting errors are caused by faulty input data rather than analyst skill, that means error rates don't reliably measure analyst skill or training effectiveness at all.

Ans is B.
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I marked B but understood that- Creator of the question wants us to attack the reasoning which is effectiveness of training program can be shown basis the data.

Let's try to stick to the reasoning...

Between B and D:

B: This directly questions the reasoning by giving alternate cause of the data which is experience and not effectiveness of training (Without any assumption)

D: This option again tries to tell how data might not conclude the training effectiveness but not does not directly talk about data and hence fails to break the link of premise to conclusion. This option requires assumption- trained analysts are not better at rectifying/highlighting errors. Corporate experience talking :grin:

Option B
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Understanding the Premise's Statistics
Let's re-examine the core numbers provided in the passage:
Group A (All Analysts): 33% have taken the training program.
Group B (Analysts making serious reporting errors): Only 8% have taken the training program.
Because 33% of all analysts are trained, but trained analysts only account for 8% of the error-makers, the argument concludes that training prevents analyst errors.

Why Option D Fails to Weaken the Argument
Option D states:
"Most serious reporting errors result from faulty data supplied to analysts rather than from mistakes made by the analysts themselves."
Now, ask: Who gets supplied with this faulty data?
Faulty data is supplied randomly across the entire company. It goes to both trained analysts and untrained analysts equally.
If faulty data is the main cause of errors, both trained and untrained analysts should fall victim to faulty data at equal rates.
Therefore, if 33% of the workforce is trained, then approximately 33% of the "faulty data errors" should occur among trained analysts!

The Disparity Remains Unexplained:
Option D fails to explain why only 8% of the error-makers are trained analysts when 33% of the overall workforce is trained. If faulty data affects everyone equally, why aren't trained analysts making 33% of those faulty-data errors?
Because Option D affects both groups equally, it leaves the core statistical gap untouched.

Why Option B Successfully Weakens the Argument
Option B states:
"Newly hired data analysts who are most likely to complete a training program are those who already have several years of experience..."
Option B introduces a Selection Bias / Alternate Cause that directly explains the statistical disparity:
Experienced analysts make fewer mistakes because of their years of experience, not because of the training course.
Because experienced people are the ones who sign up for the course, the course looks effective in the stats, but the true cause of the low error rate is their prior experience.
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