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IIT Roorkee Replies To Huge Marks Difference Between Paper 1 And 2, Calls It "Chebyshev's Inequality"

According to Stanford, the goal of Chebyshev's inequality is to bound the probability that the variable is far from its mean (in either direction).

IIT Roorkee Replies To Huge Marks Difference Between Paper 1 And 2, Calls It "Chebyshev's Inequality"
IIT Roorkee responds to JEE Advanced 2026 marks difference between Paper 1 and Paper 2.

IIT Roorkee: The Indian Institute of Technology (IIT), Roorkee, has responded to complaints about a large variation in student scores between Paper 1 and Paper 2 of the Joint Entrance Examination (JEE) Advanced 2026, saying the disparity can be explained using "Chebyshev's inequality" and "basic statistical principles" rather than any error in assessment. 

A number of JEE candidates reportedly received different marks across the two papers. Students took to social media, flagging the discrepancy after results were released last week, sharing score comparisons that suggested unusually high swings for several candidates. 

A user on X, Ananya Chopra, shared the huge difference between Paper 1 and Paper 2 marks, highlighting that a student scored -3 in Paper 1 and 104 in Paper 2. 

Replying to Ananya, Manindra Agrawal, Professor and Director, IIT Kanpur, said that such claims show a lack of understanding of basic statistics. "When there are 60,000 students, some will have large gaps in marks for two papers," he asserted, supporting his claim using Chebyshev's inequality.

A student Nikunj Gupta shared IIT Roorkee's reply that he received on his mail ID. Calling it a "misunderstanding of basic statistical principles", the institute stated: "In an examination involving nearly 60,000 candidates, it is entirely expected that some students will exhibit large differences in scores between the two papers".

"This phenomenon can be explained and quantified using established statistical results such as Chebyshev's inequality," it added.

What Is Chebyshev's Inequality?

The theorem is about the range of standard deviations around the mean, in statistics. The inequality can be applied to any probability distribution in which the mean and variance are defined.

Chebyshev's inequality is based on the concept of variance. It says that given a random variable R, then ∀ x > 0, the probability that the random variable R deviates from its expected value in either side by at least x is: P(|R−Ex(R)|>=X)<=Var(R)/X∗X

According to a reading material from Stanford University, the goal of Chebyshev's inequality is to bound the probability that the variable is far from its mean (in either direction).

According to Stanford, this inequality is still far from the actual probability. This is because Chebyshev's inequality only takes the mean and variance into account. "There is so much more information about a variable than just these two quantities," the report added.

The organising institute of the JEE Advanced 2026 further stated that "such score variations have been observed consistently in JEE (Advanced) since the introduction of the two-paper format and arise naturally from differences in question types, difficulty levels, marking schemes, and individual candidate performance across papers."

"The existence of a large score gap between the two papers is not, by itself, evidence of any irregularity. There is no indication of cheating or malpractice at any stage of the examination process, the institute added. 

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