What hypotheses exist in hypothesis testing?

In hypothesis testing, two main hypotheses are usually considered:

1. Null hypothesis (H0) — a statement that is assumed to be true by default. It usually indicates no effect or no difference.

2. Alternative hypothesis (H1 or Ha) — a statement that contradicts the null hypothesis and reflects the presence of an effect or difference.

For example, when testing the effectiveness of a new algorithm:

• H0: The new algorithm does not differ in quality from the old one.
• H1: The new algorithm is better than the old one.

The goal of hypothesis testing is to decide, based on data, whether there are sufficient grounds to reject H0 in favor of H1 at a given significance level.

There are also one-sided and two-sided hypotheses, depending on the direction of the effect check.