if our model is good then we’d expect it to be able to explain more variance than it can’t explain. In this case, the test statistic will be greater than 1 (but not necessarily significant). Similarly, larger parameters (bigger effects) that are likely to represent the population (smaller sampling variation) will produce larger test statistics.
