 Although voters will be incompetent under some (unfortunate) instances \(x\) of the facts \(\mathbf{x}\), they will plausibly be competent most of the time, i.e., under most facts instances. This suggests replacing Conditional Competence with the weaker premise of Tendency to Competence. What is this axiom? It says that competence is more often high than low. More precisely, competence exceeds 0.5 by some amount at least as often as it falls below 0.5 by this amount, for any positive amount. So, competence is 0.51 at least as often as 0.49; it is 0.55 at least as often as 0.45; etc. Formally, an individual \(i\)’s specific competence \(P(R_{i}|\mathbf{x})\) depends on the (random) facts \(\mathbf{x}\), hence is itself random. If its value exceeds \(\frac{1}{2}\), the facts can be called truth-conducive or easy for \(i\); if its value is below \(\frac{1}{2}\), the facts can be called misleading or difficult for \(i\); if its value is \(\frac{1}{2}\), the facts can be called neutral for \(i\), as they do not push in any direction. A discrete random number (such as specific competence \(P(R_{i}|\mathbf{x})\)) tends to exceed \(\frac{1}{2}\) if, for each \(\epsilon&gt;0\), it equals \(\frac{1}{2}+\epsilon\) at least as likely as \(\frac{1}{2}-\epsilon\).