We can plot the likelihood function for a model whose distributions are indexed by a one-dimensional parameter θ and literally see what the data say. We might take note of special features, such as where the function is maximized, the limits of the 1/8 likelihood interval, etc., but the best way to see what the data say, in a general way, is simply to look at the entire likelihood function.