Answer:
In statistics, the null hypothesis (denoted as H0) is a statement or assumption that suggests there is no significant difference, relationship, or effect between variables or groups being compared. It serves as a starting point for hypothesis testing and is often used to assess the validity of an alternative hypothesis.
The null hypothesis represents the idea of no change, no effect, or no difference in the population being studied. It is formulated based on prior knowledge, assumptions, or conventional wisdom. The goal of hypothesis testing is to evaluate the evidence against the null hypothesis and determine whether it should be rejected in favor of an alternative hypothesis.
In a hypothesis test, the null hypothesis is typically assumed to be true initially. The alternative hypothesis (denoted as Ha or H1) proposes a specific claim or hypothesis that contradicts or differs from the null hypothesis. The data collected and analyzed during the hypothesis test are used to assess the likelihood of the observed results occurring under the assumption of the null hypothesis being true.
If the evidence from the data strongly contradicts the null hypothesis, the null hypothesis is rejected in favor of the alternative hypothesis. This implies that there is sufficient evidence to support the claim made by the alternative hypothesis. However, if the evidence is not strong enough, the null hypothesis is not rejected, and it is not concluded that the alternative hypothesis is true.
It's important to note that failing to reject the null hypothesis does not prove the null hypothesis is true. It simply suggests that there is not enough evidence to support the alternative hypothesis. The conclusions drawn from hypothesis testing depend on the analysis, significance level, sample size, and other factors specific to the study.
