Understanding hypotheses is crucial for hypothesis testing, which is a foundation for making decisions in both business and scientific research. When you conduct a statistical test, you always start by stating two competing hypotheses: the null hypothesis and the alternative hypothesis.

The null hypothesis (often written as $H_0$) is the default or status quo assumption. It typically suggests there is no effect, no difference, or no relationship between variables. For instance, in a business context, suppose you want to test if a new website design increases conversion rates. The null hypothesis would state: "The new design does not change conversion rates compared to the old design."

On the other hand, the alternative hypothesis (written as $H_1$ or $H_a$) represents what you aim to support. It proposes that there is an effect, a difference, or a relationship. Continuing the previous example, the alternative hypothesis would be: "The new website design changes conversion rates compared to the old design." This could be a two-sided alternative (any change) or one-sided (an increase or decrease).

In scientific research, these hypotheses guide experiments and data collection. For example, if a pharmaceutical company tests a new drug, the null hypothesis might state: "The new drug has no effect on blood pressure," while the alternative hypothesis claims: "The new drug lowers blood pressure."

Setting up clear null and alternative hypotheses ensures your statistical tests are objective and your conclusions are valid.