Final answer:
The status quo hypothesis in statistical hypothesis testing is known as the null hypothesis, denoted as H0. It proposes no change or effect, and is opposed by the alternative hypothesis, Ha, which suggests there may be a measurable change or effect.
Step-by-step explanation:
The hypothesis of the status quo is typically referred to as the null hypothesis. When conducting hypothesis testing, we start by setting up a null model, denoted as H0, which suggests that there is no change or measurable effect in the variable of interest. By contrast, the alternative hypothesis, denoted as Ha, challenges the null by positing that there is a measurable effect or change.
To decide whether to reject the null hypothesis, we compare the data to this model, often using a significance level (commonly 5%) as a threshold for making Type I errors, which involve incorrectly rejecting a true null hypothesis. Should the null be rejected, it implies there's sufficient evidence to support the alternative hypothesis, without saying that the claim has been proven true or false, since the conclusion is based on probabilities, not certainties.
In summary, the null and alternative hypotheses embody opposing viewpoints about the population parameters. The null hypothesis asserts consistency or no effect, while the alternative hypothesis suggests there's a difference or effect.