In statistics, "rejecting the full null" typically refers to the process of rejecting a specific type of null hypothesis in the context of hypothesis testing. The null hypothesis (often denoted as \(H_0\)) is a statement that there is no effect or no difference, and it serves as a starting point for statistical testing.
When you encounter the term "full null," it usually refers to a comprehensive or complete null hypothesis that asserts that there is no effect or no difference across all parameters of interest. Rejecting the full null hypothesis means that the data provide sufficient evidence to conclude that at least one of these assertions is false.
For example, in a multiple regression context where you have several coefficients being tested, the "full null" might state that all coefficients are equal to zero (i.e., no independent variable has an effect on the dependent variable). Rejecting this full null would suggest that at least one coefficient is not equal to zero, indicating that at least one of the predictors is significantly associated with the response variable.
In practice, rejecting the full null often involves conducting a statistical test (like ANOVA, a t-test, or a chi-squared test) and comparing the p-value of the test against a predetermined significance level (commonly set at 0.05). If the p-value is less than the significance level, you reject the full null hypothesis.
In summary, "rejecting the full null" means concluding that the hypothesis stating no effect or no difference across the board is not supported by the evidence from the data.
1 answer