 So, we’ll measure the Quality of the Information an outcome would yield in distinguishing between two hypotheses as the base-2 logarithm of the likelihood ratio. This is clearly a symmetric measure of the outcome’s evidential strength at distinguishing between the two hypotheses. On this measure hypotheses \(h_i\) and \(h_j\) assign the same likelihood value to a given outcome \(o_{ku}\) just when \(\QI[o_{ku} \pmid h_i /h_j \pmid b\cdot c_k] = 0\). Thus, QI measures information on a logarithmic scale that is symmetric about the natural no-information midpoint, 0. This measure is set up so that positive information favors \(h_i\) over \(h_j\), and negative information favors \(h_j\) over \(h_i\).