Hypothesis Testing
Explore the key concepts and procedures of hypothesis testing in this lesson. Understand how to formulate null and alternative hypotheses, interpret p-values and significance levels, and recognize types of errors in statistical tests. This lesson equips you with the skills to apply hypothesis testing effectively for data-driven decision-making using R.
We'll cover the following...
Important concepts and terms
Hypothesis testing is a statistical technique used to make inferences about a population based on a data sample. In hypothesis testing, we use sample data to test a claim about a population. The claim, also known as the null hypothesis (), is compared against an alternative hypothesis (). By applying statistical methods, we decide whether to accept or reject the null hypothesis based on the evidence from the sample.
The null hypothesis always assumes that the effect of the independent variables on the dependent variables is insignificant. Our significance tests always seek answers to this question: “What would be the extreme probability if the null hypothesis were true?”
In short, we try to find if the current situation is too extreme for our null hypothesis.
Let’s explain some terms used in hypothesis ...