Normalization in SSC CGL: Government jobs are always the golden goose for those wanting a stable career. SSC CGL, with its varied posts and countless perks, is one of these. This blog is actually going to tackle one aspect of the marking scheme – the normalization process. Let’s take a look at what that is and why it’s in use.
What is Normalization in SSC CGL 2025
SSC conducts its exams in multiple shifts due to the large number of applicants. Naturally, the difficulty level of one shift may not be the same as another. Some shifts may have slightly tougher questions, while others may be relatively easier. To ensure that no candidate is unfairly advantaged or disadvantaged because of this, normalization is applied. In simple terms, normalization means adjusting scores so that candidates from all shifts can be compared fairly on a common scale.
Earlier Method of Normalization (2019 Formula-Based System)
Until recently, SSC followed a formula-based normalization method, which considered:
- The average marks of top 0.1% candidates across all shifts.
- The mean and standard deviation of marks in each shift.
- The actual marks obtained by the candidate in his/her shift.
This formula calculated an adjusted score by comparing how a candidate performed relative to everyone else across all shifts. While this method ensured fairness, it relied heavily on averages and score distribution.
New Method of Normalization (Equipercentile Method – 2025)
From June 2025, SSC has officially adopted a new normalization system known as the Equipercentile Method. Under this method, instead of using averages and standard deviations, SSC now considers percentiles of candidates in each shift. Here’s how it works:
- A candidate’s percentile score is calculated based on how they performed compared to others in the same shift.
- For example, if a candidate scored better than 80% of examinees in Shift 1, and another candidate scored better than 80% in Shift 2, their performances are treated as equivalent even if raw marks differ.
- This ensures that relative performance, not absolute marks, determines fairness across shifts.
This approach removes the dependency on raw difficulty differences and provides a fairer, shift-wise comparison.
Why the Equipercentile Method?
The equipercentile method was introduced by SSC in 2025 to ensure a more transparent and fair evaluation process. It focuses on comparing candidates’ relative performance within their own shift rather than relying only on raw marks.
| Key Point | Explanation |
| Fair comparison | Candidates are judged on their ranking within their shift, ensuring fairness across all sessions. |
| Tougher shifts balanced | Those appearing in more difficult shifts are not disadvantaged compared to easier shifts. |
| Content variation handled | Differences in question difficulty or content across shifts are accounted for effectively. |
| Reliable outcomes | Provides consistent and trustworthy results for large-scale exams like SSC CGL. |
Difference Between Earlier Method and New Method of Normalization
To better understand the change, the key differences between the earlier normalization method (2019) and the newly adopted equipercentile method (2025) are as follows:
| Aspect | Earlier Method (2019 Formula) | New Method (2025 – Equipercentile) |
| Basis of Calculation | Used mean, standard deviation, and top 0.1% scores across shifts | Uses percentile/rank position of candidates within their own shift |
| Focus | Adjusted scores based on average performance and variation in difficulty | Compares candidates by their relative standing (percentile) in each shift |
| Example | If one shift was harder, scores were statistically raised using averages and spread | A candidate at 80th percentile in one shift is matched with 80th percentile in another shift |
| Treatment of Difficulty Variation | Adjusts marks numerically for harder/easier shifts | Matches candidates by performance rank, making difficulty differences less impactful |
| Transparency | Complex formula, harder for candidates to interpret | Easier to understand as it is based on percentile ranking |
| Reliability | Effective but sometimes debated for fairness when content varied | More robust and fair, especially for large-scale exams like SSC CGL |
Need for Normalization in SSC CGL
The basic aim of normalization is to compare the performance of the exam takers on an unbiased scale. This way, the result will be free of influencing factors like difficulty level. Normalization makes sure that candidates who face a difficult shift will not be at a disadvantage compared to the ones who got an easier shift. It greatly alleviates tension for students to know they will not suffer because of unbalanced difficulty.
FAQs
Ans. Normalization refers to the process of finding the average marks scored in the shifts of a multi-shift exam and determining the difference in difficulty by comparing these marks. A higher average means an easier shift, while a lower average means a difficult shift. Officials then make sure to adjust the marks to equate the difficulty level.
Ans. Normalization is an essential process for SSC CGL. It makes sure no candidate suffers because of unbalanced difficulty compared to other shifts.
Ans. The process of normalization was introduced in SSC CGL in 2019.
Ans. SSC shifted to the equipercentile method in 2025 because it provides a more accurate and fair comparison by ranking candidates within their shifts and matching equivalent percentiles across all shifts.
Ans. In this method, candidates are compared based on their relative position (percentile) in their own shift. For example, a candidate in the 80th percentile in one shift is matched with a candidate in the 80th percentile in another shift, ensuring fairness despite varying difficulty.
Ans. The earlier method (2019) adjusted scores using average marks, top 0.1% scores, and standard deviation of each shift. It relied more on statistical averages, while the new method relies on percentile ranking.
Ans. The equipercentile method is considered fairer because it reduces the impact of variations in difficulty or content across shifts and better reflects a candidate’s relative performance.
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Hi, I’m Tripti, a senior content writer at Oliveboard, where I manage blog content along with community engagement across platforms like Telegram and WhatsApp. With 3+ years of experience in content and SEO optimization related to banking exams, I have led content for popular exams like SSC, banking, railways, and state exams.