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UGC NET Normalisation of Marks 2025: Check Result Preparation Criteria

Last update: Jun 28, 2025

UGC NET Normalisation of Marks 2025: UGC NET 2025 June session is being conducted by the National Testing Agency (NTA) from June 25 to June 28 in different shifts. Across these days, candidates reported a mixed level of difficulty, with most describing Paper 1 as ranging from easy to moderate, and some shifts even being moderate to difficult. Questions were seen from expected topics like Data Interpretation, Research, Communication Models, Indian Logic, and SDGs, along with frequent questions on movies and general awareness.

 

Due to the multi-shift nature of UGC NET 2025 Exam, NTA will apply the normalisation method to ensure fairness across different difficulty levels. This process adjusts scores statistically so that no candidate is disadvantaged due to variation in exam toughness. The following analysis presents shift-wise feedback, topic trends, and difficulty level insights based on student responses.

 

Table of Contents

  1. UGC NET 2025 Normalisation of Marks: Why It is Required?
  2. UGC NET 2025 Normalisation Method: Formula Used by NTA
  3. UGC NET 2025 Normalisation Method: Example
  4. Result Preparation Process for UGC NET 2025
  5. UGC NET Cut-Off 2025: Determination Criteria
  6. Points to Remember About UGC NET Normalisation 2025
  7. Wrapping Up

 

UGC NET 2025 Normalisation of Marks: Why It is Required?

UGC NET June 2025 exam is ongoing in a multi-shift format over several days, with each subject appearing in multiple sessions. While the exam pattern and syllabus remain the same, it is not always possible to maintain uniform difficulty across all shifts. To ensure fairness in evaluation, the National Testing Agency (NTA) uses a percentile-based normalisation method.

 

Multi‑shift Format

The UGC NET June 2025 exam is being conducted over five days, with two shifts per day, leading to multiple sessions for the same subjects. As a result, candidates faced different versions of the question paper, even though the format and syllabus remained consistent.

 

Unequal Difficulty

Despite NTA’s efforts to standardise question papers, it is practically impossible to ensure identical difficulty levels across all shifts. Some sessions may be marginally easier or tougher than others, potentially affecting raw scores unfairly.

 

Fair selection

Percentile scores are calculated for each shift to ensure fair selection. NTA uses them to:

  • Compile common merit lists
  • Set category- and subject-wise cut-offs
  • Determine eligibility for JRF and Assistant Professor roles

This ensures a fair, standardised, and unbiased evaluation process for all candidates, regardless of the exam shift they were assigned.

 

Solution: Percentile‑Based Normalisation

To address this, the National Testing Agency (NTA) applies the equi-percentile normalisation method. In this system:

  • A candidate’s raw score is converted into a percentile score relative to all candidates in the same shift.
  • This percentile reflects how a candidate performed compared to peers in the same session, neutralising shift-level difficulty variation.

 

UGC NET 2025 Normalisation Method: Formula Used by NTA

Before diving into the numeric examples, here is the core formula that NTA applies for each multi-shift paper:

 

Percentile Score = 100 × (Number of candidates in the session with raw score ≤ your raw score) ÷ Total number of candidates who appeared in the session.

 

– Scores are calculated up to seven decimal places to minimise ties.– The topper in each shift (highest raw score) receives a percentile of 100.0000000.– Percentile scores, not raw marks, are used to compile the final NTA scores for result preparation.

Formula Components Explained

  • Numerator: Count of all candidates in that specific shift whose raw marks are equal to or less than the candidate’s raw marks.
  • Denominator: Total number of candidates who appeared in that shift.
  • Scaling Factor (100): Converts the fraction into a percentile on a 0–100 scale.

This method converts disparate raw-mark distributions from different sittings into a common percentile scale, ensuring fairness across all shifts.

 

UGC NET 2025 Normalisation Method: Example

Below is an excerpt from Appendix VIII (Session 3: Day 2, Shift 1) showing how a handful of raw scores translate into percentile scores.

Candidate

Raw Score

Percentile Score

A

331

100.0000000

B

121

90.1224411

C

41

50.4549194

D

25

31.7040120

E

–15

1.1034216

Table: Mapping of raw marks to percentile scores for five representative candidates in one session; raw marks range from –15 to 331 and are normalized to a 0–100 scale.

 

Result Preparation Process for UGC NET 2025

Accurate UGC NET 2025 Result preparation process hinges on transforming raw marks into normalised percentiles before determining cut-offs and merit lists. Grasping each stage clarifies how individual performance converts into eligibility status.

 

Before reviewing the detailed steps, note that normalised marks usage ensures that percentile scores—rather than raw attempts—govern the ranking and final cut-off calculation.

  • Step 1: Raw score calculationCompute total correct and incorrect responses to derive the candidate’s raw marks.
  • Step 2: Application of normalisationConvert raw scores to percentiles using the NTA’s formula, equalising shift-wise difficulty.
  • Step 3: Final score calculationAggregate percentile scores from Paper I and Paper II to form the NTA score.
  • Step 4: Cut-off declarationDeclare category- and subject-wise cut-offs based on the distribution of normalised percentiles.

 

UGC NET Cut-Off 2025: Determination Criteria

Past cut-offs help candidates estimate score-to-rank dynamics by showing the percentile thresholds required in previous cycles.

 

Category- and paper-wise cut-offs are set as the lowest normalised percentile corresponding to the minimum qualifying marks across all shifts for that category. NTA first determines, for each shift, the percentile equivalent of the required aggregate percentage (e.g., 40% for UR, 35% for reserved), then adopts the minimum of these percentiles as the eligibility cut-off for every candidate.

 

No separate tie-breaking policy is specified in the UGC NET information bulletin; any candidate whose normalised score meets or exceeds the cut-off percentile is declared qualified on equal footing.

Category

Dec 2023 Cycle Cut-Off

June 2024 Cycle Cut-Off

Unreserved (UR)

40 percentile

40 percentile

OBC-NCL / EWS

35 percentile

35 percentile

SC / ST / PwD

30 percentile

30 percentile

Table: Minimum normalised percentile cut-offs by category for the last two UGC NET cycles

 

Read: UGC NET 2025 Cutoff

 

Points to Remember About UGC NET Normalisation 2025

A number of myths surrounding UGC NET normalisation tend to mislead aspirants and require clarification prior to exam preparation or result interpretation. The points given below summarize the essence behind the conversion from raw marks to a uniform scoring system:

  • Raw scores are not used directly for merit: Only percentile scores derived through normalisation determine eligibility.
  • Shift-wise fairness ensured: Normalisation adjusts for session-to-session difficulty variation by mapping raw marks onto a 0–100 percentile scale.
  • High precision percentiles: Scores are calculated up to seven decimal places to minimise ties.
  • Cut-off based on percentiles: Category- and paper-wise cut-offs correspond to minimum qualifying percentiles, not raw marks.
  • Separate normalization per paper: Paper I and Paper II percentiles are computed independently before aggregation.
  • Final ranking uses aggregated percentiles: Combined NTA scores from both papers decide merit lists.

 

Wrapping Up

UGC NET uses a strict statistical normalisation process, as recommended by the NTA, to standardize performance measures across different exam sessions. Converting raw marks to accurate percentile scores, the process preserves equality of cut-off and merit rankings. Candidates must focus on steady score improvement for both papers, as resulting percentiles automatically depict relative position and keep in place diligent preparation instead of session ambiguity determining final results.

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