What If The Exam Marks Are Not Normally Distributed?

Data Transformation — Normalisation and Standardisation using Python Scikit-Learn.Usually, when I tell you a student has got 90 marks, you would think this is a very good student.

Usually, when I tell you a student has got 90 marks, you would think this is a very good student. Instead, if I say the marks are 75, that probably means the student might be average. However, as a Data Scientist/Analyst, we need at least ask two questions immediately:

1. Is the full mark 100?
2. What’s the distribution of the marks in the class?

The first question is obviously important and perhaps everyone would ask because 90/150 is definitely not better than 75/100. The second question is a little bit subtle and possibly only a “data person” will have this sensitivity.

In fact, in order to make sure an exam having its results normally distributed in the class, it is quite common to select exam questions as follows:

1. Basic and easy questions — 70%
2. Extended questions that will need a deep understanding of the knowledge — 20%
3. Difficult questions that will need to be solved with not only adequate knowledge but also some insights — 10%

What if we have 100% easy questions or 100% difficult questions? If so, we’re very likely to have results that are not normally distributed in a class.

Why Do We Need to Normalise/Standardise Data?

Then, we have our main topic now. I have been a tutor at a University for 5 years. It sometimes cannot be guaranteed that the exam questions are precisely followed the above proportions. To make sure it is fair to all the students, in other words, not too many students failed or too many students got A grades, sometimes we need to normalise the marks to make sure it follows the normal distribution.

python statistics data-transformation data-analysis data-science data analysis

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