close

Maths in AI – Statistics and Probability

June 29, 2025 · By @mritxperts

Total Hours: 25 | Marks: 10


Introduction

Maths plays a very important role in Artificial Intelligence. To make AI smart and accurate, we use some basic concepts from Statistics and Probability.

These maths topics help AI understand data, find patterns, and make predictions.


What is Statistics?

Statistics is a branch of mathematics that deals with collecting, organizing, analyzing, and interpreting data.

In simple words, statistics helps us understand large amounts of data by using numbers.


Important Terms in Statistics

1. Mean (Average)

Mean is the average of a set of numbers.
Formula:
Mean = (Sum of all values) ÷ (Total number of values)

Example:
If marks are 50, 60, 70
Mean = (50 + 60 + 70) / 3 = 180 / 3 = 60


2. Median

Median is the middle value when the numbers are arranged in order.

Example 1 (odd number of values):
50, 60, 70 → Median = 60 (middle value)

Example 2 (even number of values):
40, 50, 60, 70 → Median = (50 + 60)/2 = 55


3. Mode

Mode is the number that appears most often in the data.

Example:
45, 60, 45, 70, 80 → Mode = 45 (because it appears twice)


4. Standard Deviation (SD)

Standard deviation tells us how spread out the numbers are from the mean.

Note for students: We do not need to calculate SD in detail in Class 9. Just understand the concept.


Types of Data in Statistics

1. Numerical Data

Data that involves numbers.

Example:
Marks of students, ages, height, weight

2. Categorical Data

Data that represents categories or groups.

Example:


Graphical Representation of Data

To understand data better, we show it using graphs or charts.

Common graphs include:

Graphs make it easy to visualize and analyze the data.


What is Probability?

Probability is the chance or likelihood of an event happening.

It is a number between 0 and 1.

Formula:
Probability = (Number of favorable outcomes) ÷ (Total number of outcomes)


Example 1: Tossing a Coin


Example 2: Rolling a Dice


Types of Probability

1. Theoretical Probability

This is based on logic and known outcomes.

Example: Tossing a coin or rolling a dice

2. Experimental Probability

This is based on real experiments or data collected.

Example: Toss a coin 100 times and note how many times you get heads.

Formula:
Experimental Probability = (Number of times event happened) ÷ (Total number of trials)


Importance of Statistics and Probability in AI

Examples:


Suggested Classroom Activities and Practicals

1. Dice Game

Roll a dice 50 times and record the results.

2. Mini Survey and Data Analysis

Ask students to collect data on any topic.
Example: Favorite food of classmates.

3. Number Pattern Game

Give students sets of numbers and ask them to:


Summary of the Unit