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🎓 Lecture 2 - What is Statistics? | Types of Statistics & Population vs Sample

Welcome to Lecture 2 of the Statistics and Probability for Data Science Complete Course (2025)! In this lecture, we cover the fundamentals of statistics, the different types of statistics, and the concept of population vs sample, helping you build a strong foundation for data analysis.

📘 Topics Covered in This Lecture:

1) What is Statistics? – Definition and real-life examples

2) Types of Statistics – Descriptive vs. Inferential

3) Population vs Sample – Understanding the difference and its importance in analysis

🧠 Key Concepts Explained:

What is Statistics?

Statistics is the branch of mathematics that deals with collecting, organising, analysing, and interpreting data to make decisions or predictions.

Examples of Statistics in Real Life:

1) Calculating the average income of a city

2) Measuring the pass percentage of students in an exam

3) Estimating the average temperature of a place over a month

4) Predicting future sales of a company based on past data

Types of Statistics:

1) Descriptive Statistics: Summarises and describes data. Helps simplify large datasets into understandable summaries.

2) Inferential Statistics: Makes predictions or inferences about a population using a sample. Involves probability theory to draw conclusions about larger groups.

Population vs Sample:

1) Population: The complete set of individuals or items that you want to study.

2) Sample: A smaller subset of the population used to make inferences about the population.

Materials used here -
https://drive.google.com/drive/folder...

🎯 Goal of This Lecture:

By the end of this lecture, you’ll understand what statistics is, the difference between descriptive and inferential statistics, and how population and sample concepts are used in real-world data analysis.

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