The “normalisation process” mentioned in the Railway Board letter click for RB Letter is a statistical adjustment method used when an exam is conducted in multiple sessions/days 📊.
👉 Why is it needed?
When the same exam is held on different dates (like 24 May & 31 May), the difficulty level may not be exactly the same:
One paper may be slightly tough
Another may be slightly easy
👉 Without correction, candidates writing the easier paper may get an unfair advantage.
✅ Simple Meaning
Normalisation = Adjusting marks so that all candidates are treated fairly, regardless of which day they wrote the exam.
🔍 Practical Example
Example 1: Two Exam Days
Day 1 (24 May) → Paper is tough
Average marks: 40/100
Day 2 (31 May) → Paper is easy
Average marks: 60/100
Now compare two candidates:
Candidate A (Day 1): scored 50
Candidate B (Day 2): scored 60
👉 At first glance, B looks better.
👉 But actually:
A scored above average in a tough paper
B scored average in an easy paper
✅ After normalisation:
A’s marks may be increased
B’s marks may be slightly reduced or adjusted
👉 So that both are judged on a common scale
Example 2: Real-Life Analogy 🎯
Think of it like this:
One teacher sets a very difficult question paper
Another sets an easy paper
If we compare raw marks directly, it is unfair.
So we adjust marks based on overall performance of each group.
⚙️ How it works (Conceptually)
Railways (like RRB exams) use:
Average marks of each session
Highest marks
Distribution of scores
👉 Then apply a formula to bring all candidates onto a uniform level
(Exact formula is technical — not needed for exam purpose)
🎯 Key Takeaways (Exam-Oriented)
Used when exam is conducted in multiple shifts/dates
Ensures fairness & parity
Based on relative performance, not just raw marks
Common in RRB, SSC, Banking exams
⚠️ Important Insight for LDCE Candidates
👉 Don’t worry about:
Which date you get
Whether paper is tough or easy
👉 Focus on:
Performing better than others in your session
Because finally, your rank depends on normalised score, not raw score ✔️

