 Some option, prospect, or other object is being evaluated (“graded”) in terms of some criterion. A painting might be evaluated in terms of beauty: is it “beautiful”, “neutral”, or “ugly”? An academic applicant might be graded in terms of research skills. Let \(\mathcal{A}\) be a finite set of possible grades, linearly ordered from “highest” to “lowest”. The state \(\mathbf{s}\) is the true value or grade in \(\mathcal{A}\) (\(=\mathcal{S}\)). Each group member assigns a grade. The resulting grading profile \((a_{1},\ldots,a_{n})\) is aggregated into the median grade \(a=F(a_{1},\ldots,a_{n})\), i.e., the middle grade after putting \(a_{1}, \ldots, a_{n}\) into a weakly increasing order \(a_{1}^{\prime}, \ldots, a_{n}^{\prime}\) (to ensure medians exist, let \(n\) be odd). If, for instance, a painting is evaluated twice as “beautiful” and once as “ugly”, where \(n=3\), then the median evaluation is “beautiful”. Given an instance \(s\) of the state, consider a voter \(i\)’s correctness probability \(P(\mathbf{v}_{i}=s|s)\), over-valuation probability \(P(\mathbf{v}_{i}&gt;s|s)\), and under-valuation probability \(P(\mathbf{v}_{i}&lt;s|s)\). The absolute difference between the over- and under-valuation probability defines \(i\)’s bias, i.e., tendency to over- or under-value. Morreau (2021) proves a “grading-jury theorem”: if, conditional on any state, the grades \(\mathbf{v}_{1},\mathbf{v}_{2},\ldots\) are (i) independent, (ii) homogeneously distributed, and (iii) sufficiently unbiased (i.e., with bias below the correctness probability), then the correctness probability of the median evaluation increases in (odd) group size and converges to  certainty.[30]  The correctness probability can become very small, provided it exceeds the bias. Having small bias seems particularly difficult if the true value is very high or very low, since extreme values are easily under- or over-estimated.