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Introduction

Lecture video introduction

9.1 Null and Alternative Hypotheses

Lecture video 9.1

Example 9.1

Try It 9.1

Example 9.2

Try It 9.2

Example 9.3

Try It 9.3

9.2 Outcomes and the Type I and Type II Errors

Lecture video 9.2

Example 9.4

Try It 9.4

Example 9.5

Try It 9.5

Example 9.6

Try It 9.6

Example 9.7

Try It 9.7

9.3 Probability Distribution Needed for Hypothesis Testing

Lecture video 9.3

Assumptions

Hypothesis Test for the Mean

P-Value Approach

One and Two-tailed Tests

Effects of Sample Size on Test Statistic

A Systematic Approach for Testing a Hypothesis

9.4 Full Hypothesis Test Examples

Lecture video 9.4

Tests on Means

Example 9.8

Try It 9.8

Example 9.9

Try It 9.9

Example 9.10

Try It 9.10

Hypothesis Test for Proportions

Example 9.11

Try It 9.11

Example 9.12

Try It 9.12

Example 9.13

Try It 9.13

Example 9.14

Try It 9.14

Quizzes

Welcome to 'Introduction to Hypothesis Testing with One Sample,' a comprehensive course designed to demystify one of the most fundamental concepts in statistics. Whether you are a student, a data enthusiast, or a professional looking to make data-driven decisions, understanding how to test hypotheses is crucial.

Welcome to 'Introduction to Hypothesis Testing with One Sample,' a comprehensive course designed to demystify one of the most fundamental concepts in statistics. Whether you are a student, a data enthusiast, or a professional looking to make data-driven decisions, understanding how to test hypotheses is crucial. This course takes you step-by-step through the entire process of one-sample hypothesis testing. We begin by laying a strong foundation with null and alternative hypotheses, ensuring you know exactly how to frame your research questions. From there, we dive into the potential pitfalls of decision-making by exploring Type I and Type II errors. You will also learn about the probability distributions required for these tests, the assumptions you must meet, and the critical differences between one-tailed and two-tailed tests. As you progress, the course transitions from theory to practice. You will explore the P-value approach and understand how sample size impacts your test statistics. Finally, we bring it all together with full, real-world hypothesis test examples covering both means and proportions. Packed with interactive 'Try It' exercises and comprehensive quizzes, this course ensures you not only learn the theory but also apply it confidently. By the end of this journey, you will possess a systematic approach to hypothesis testing, empowering you to draw accurate, statistically significant conclusions from your data.

Chapter 9 Introduction to Hypothesis Testing with One Sample

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            <p>Hypothesis testing is a fundamental statistical tool used to make informed decisions and draw conclusions about a population based on sample data. This course provides a comprehensive exploration of how to formulate, execute, and interpret these tests using a single sample. You will begin by establishing a strong foundation in setting up statistical inquiries, specifically focusing on how to define null and alternative hypotheses. From there, the curriculum delves into the potential outcomes of your tests, highlighting the critical concepts of Type I and Type II errors so you can fully understand the risks, probabilities, and implications of drawing incorrect statistical conclusions.</p>

<p>As you advance through the modules, you will explore the probability distributions essential for hypothesis testing and learn the core assumptions required for accurate analysis. The course offers a systematic approach to evaluating data, guiding you through the P-value method, the differences between one-tailed and two-tailed tests, and the effects of sample size on test statistics. Finally, you will apply these theoretical concepts to full hypothesis test examples, covering both tests on means and tests for proportions. Supported by numerous practical examples, interactive "Try It" exercises, and comprehensive quizzes, this course will equip you with the analytical skills necessary to confidently apply one-sample hypothesis testing to real-world data scenarios.</p>
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