The statistical practice of hypothesis testing is widespread not only in statistics, but also throughout the natural and social sciences. When we conduct a hypothesis test there a couple of things that could go wrong. There are two kinds of errors, which by design cannot be avoided, and we must be aware that these errors exist. The errors are given the quite pedestrian names of type I and type II errors.
What are type I and type II errors, and how we distinguish between them? Briefly:
Type I errors happen when we reject a true null hypothesis.Type II errors happen when we fail to reject a false null hypothesis.We will explore more background behind these types of errors with the goal of understanding these statements.
HYPOTHESIS TESTING
The process of hypothesis testing can seem to be quite varied with a multitude of test statistics. But the general process is the same. Hypothesis testing involves the statement of a null hypothesis, and the selection of a level of significance. The null hypothesis is either true or false, and represents the default claim for a treatment or procedure. For example, when examining the effectiveness of a drug, the null hypothesis would be that the drug has no effect on a disease.
