The actual hypothesis testing begins by considering two hypotheses. They are termed the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints.

The null hypothesis, denoted by H₀ is a statement of no difference between the variables—they are not related. This can often be considered the status quo. As a result if you cannot accept the null, it requires some action.

The alternative hypothesis, denoted by H₁ or H_a, is a claim about the population that is contradictory to H₀ and what we conclude when we reject H₀. This is usually what the researcher is trying to prove.

Since the null and alternative hypotheses are contradictory, one must examine evidence to determine whether e to reject the null hypothesis or not. The evidence used is in the form of sample data.

After deciding which hypothesis the sample data supports, a decision can be made. There are two options for a decision. They are "reject H₀" if the sample information favors the alternative hypothesis or "do not reject H₀" or "decline to reject H₀" if the sample information is insufficient to reject the null hypothesis.

This text is adapted from Openstax, Introductory Statistics, Section 9.1 Null and Alternative Hypothesis