In hypothesis testing, we have two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis represents the default position, often stating that there is no difference or no effect. On the other hand, the alternative hypothesis proposes a different outcome, suggesting that there is a significant difference or effect.

To better understand this concept, let's consider an example. Suppose we want to determine if a new teaching method improves students' test scores. The null hypothesis would state that there is no difference in test scores between the new teaching method and the traditional method. The alternative hypothesis would propose that there is a significant difference in test scores.

It's crucial to formulate the null and alternative hypotheses correctly, as they guide the entire hypothesis testing process. The null hypothesis is typically assumed to be true until there is sufficient evidence to reject it in favor of the alternative hypothesis.