Hypothesis Testing

Course Content
MODULE 1: INTRODUCTION TO DATA SCIENCE
In this module, students will learn the fundamentals of Data Science, which combines statistics, mathematics, and computer science to extract insights from data.
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Overview of Data Science
Data Science Lifecycle
TEST ON INTRODUCTION TO DATA SCIENCE
10:00
MODULE 2: PRACTICING THE BASICS
This module will give students foundational knowledge and hands-on experience in programming for data science using Python.
0/4
Python for Data Science
Introduction to SQL
Introducing the SELECT Statement for Retrieving Data
TEST ON PYTHON AND INTRODUCTION TO SQL
10:00
MODULE 3: STATISTICS AND PROBABILITY
This module on Statistics and Probability provides a comprehensive introduction to essential concepts and techniques.
0/10
Introduction to Statistics
Data Collection and Sampling Techniques
Descriptive Statistics
Probability Basics
Conditional Probability
Discrete Probability Distributions
Continuous Probability Distributions
Hypothesis Testing
Correlation and Regression
TEST ON STATISTICS AND PROBABILITY
10:00
MODULE 4: MATHEMATICAL FOUNDATIONS
The Mathematical Foundations module provides a solid understanding of linear algebra and calculus concepts that are essential for data science and machine learning.
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Linear Algebra
Calculus
TEST ON MATHEMATICAL FOUNDATIONS
19:00
MODULE 5: EXPLORATORY DATA ANALYSIS (EDA)
This module on Exploratory Data Analysis (EDA) emphasizes the importance of data cleaning and visualization in the data analysis process.
0/3
Data Cleaning
Data Visualization
QUIZ ON EXPLORATORY DATA ANALYSIS (EDA)
10:00
Introduction to Data Science and Analytics
About Lesson

Hypothesis Testing

Overview: This section covers the process of hypothesis testing in statistics.

  • What is Hypothesis Testing?
    • A method for testing a claim or hypothesis about a parameter in a population using sample data.
  • Steps in Hypothesis Testing:
    • Formulate Null and Alternative Hypotheses: The null hypothesis (H0) represents no effect or no difference, while the alternative hypothesis (H1) represents the opposite.
    • Choose a Significance Level (α): Commonly set at 0.05 or 0.01.
    • Calculate the Test Statistic: Based on the sample data.
    • Make a Decision: Compare the test statistic to a critical value to accept or reject the null hypothesis.
  • Real-Life Example:
    • A company may test whether a new advertising campaign leads to higher sales compared to the previous campaign by conducting a hypothesis test.
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