Hypothesis Testing

With examples in R

Introduction

Hypothesis testing is a fundamental concept in statistics used to make inferences about populations based on sample data. It involves formulating a hypothesis about the population parameter, collecting sample data, and using statistical methods to assess the evidence against the null hypothesis. In this chapter, we will explore various hypothesis testing procedures, step-by-step guidelines for conducting hypothesis tests, interpretation of results, and practical examples using the R programming language.

Understanding Hypotheses

Before diving into hypothesis testing procedures, it’s essential to understand the two types of hypotheses:

Null Hypothesis (H0): It represents the status quo or the hypothesis of no effect, no difference, or no association.

Alternative Hypothesis (Ha): It represents what we are trying to find evidence for; it is typically the opposite of the null hypothesis.

The Importance of Sample Statistics

Sample statistics are numerical values calculated from sample data that provide information about the population parameter. Common sample statistics include the sample mean, sample proportion, sample standard deviation, etc. These statistics serve as estimators for population…