Introduction: Central Limit Theorem

Welcome to the session on ‘Central Limit Theorem’. In the last session, you learnt about probability density functions, specifically normal and standard normal distributions.

In this session

You will learn what a sample is and why it is so error-prone. You will then understand how to quantify this error made in sampling using a popular theorem in statistics, called the central limit theorem.


There are no prerequisites for this session, other than, of course, your knowledge of what was discussed in the previous three sessions.

Guidelines for in-module questions

The in-video and in-content questions for this module are not graded. Note that graded questions are given on a separate page labelled ‘Graded Questions’ at the end of this session. These questions will adhere to the following guidelines:

First Attempt Marks Second Attempt Marks
Questions with 2 Attempts 10 5
Questions with 1 Attempt 10 0

People you will hear from in this session

Subject Matter Expert

Tricha Anjali

Associate Professor, IIIT- B

The International Institute of Information Technology, Bangalore, also known as IIIT-B, is one of India’s foremost graduate schools. Through its Integrated M.Tech., M.Tech., M.S. (Research) and PhD programs in the IT space, it focuses equally on innovation and education.

Reference Ebook

Statistical Inference for Data Science by Brian Caffo

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