Expected Percentile for 60 Marks in MH B.Sc Nursing CET 2025
A candidate likely to achieve an expected percentile of 92.3+ for 60 Marks in MH B.Sc Nursing CET 2025. The calculation is based on previous data and is tentative.
Expected Percentile for 60 Marks in MH B.Sc Nursing CET 2025: In the case of MH B.Sc Nursing CET 2025, scoring 60 marks is likely to have a percentile of 92.3 or above. A candidate with this percentile has performed better than about 92.3% of test takers and thus has a strong competitive standing. The percentile is derived from the performance of all candidates in relation to each other and with regard to the level of difficulty of the exam and the number of candidates who had appeared and scored. It is important to understand the expected percentile for specific marks in MH B.Sc Nursing CET 2025. The knowledge of marks vs expected percentile in the MH B.Sc Nursing CET 2025 gives candidates a better picture of how they will score in the exam and allows them to measure their chances of getting admission into some of the best nursing colleges in Maharashtra.
MH B.Sc Nursing CET Expected Marks and Percentile 2025 | MH B.Sc Nursing CET Offical Answer Key Expected Release Date 2025 |
Expected Percentile for 60 Marks in MH B.Sc Nursing CET 2025
Candidates can check the below table to know the Expected Percentile for 60 Marks in MH B.Sc Nursing CET 2025:
Marks | Expected Percentile |
69 marks | 96.4+ percentile |
68 marks | 96.3+ percentile |
67 marks | 96.1+ percentile |
66 marks | 96+ percentile |
65 marks | 95.3+ percentile |
64 marks | 95+ percentile |
63 marks | 94.6+ percentile |
62 marks | 94+ percentile |
61 marks | 93+ percentile |
60 marks | 92.3+ percentile |
Knowing this correlation between expected marks and percentile after MH B.Sc Nursing CET 2025 exam, helps candidates strategise better during counselling stage as admissions are generally based on ranks or percentile as opposed to raw scores. Expected percentile knowledge is also important to be used as a benchmark and goals during preparation. For example, if a candidate desires to be within the top 10%, they can use this data to determine what minimum score and percentile they will need to secure. The data can also help to reduce anxiety after the exam, as candidates are capable of estimating where they stand before the results come out officially.
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