One approach to risk-limiting audits (RLAs) compares randomly selected cast vote records (CVRs) to votes read by human auditors from the corresponding ballot cards. Historically, such methods reduce audit sample sizes by considering how each sampled CVR differs from the corresponding true vote, not merely whether they differ. Here we investigate the latter approach, auditing by testing whether the total number of mismatches in the full set of CVRs exceeds the minimum number of CVR errors required for the reported outcome to be wrong (the "CVR margin"). This strategy makes it possible to audit more social choice functions and simplifies RLAs conceptually, which makes it easier to explain than some other RLA approaches. The cost is larger sample sizes. "Mismatch-based RLAs" only require a lower bound on the CVR margin, which for some social choice functions is easier to calculate than the effect of particular errors. When the population rate of mismatches is low and the lower bound on the CVR margin is close to the true CVR margin, the increase in sample size is small. However, the increase may be very large when errors include errors that, if corrected, would widen the CVR margin rather than narrow it; errors affect the margin between candidates other than the reported winner with the fewest votes and the reported loser with the most votes; or errors that affect different margins.

翻译：风险限制审计（RLA）的一种方法是将随机选取的投票记录（CVR）与审计员从相应选票卡中读取的选票进行比较。传统上，此类方法通过考虑每个抽样的CVR与真实投票之间的差异程度（而不仅仅是是否不同）来减少审计样本量。本文研究后一种方法，即通过检验完整CVR集中不匹配的总数是否超过导致报告结果错误所需的最小CVR错误数（即“CVR差额”）来进行审计。该策略使得审计更多社会选择函数成为可能，并在概念上简化了RLA，从而比某些其他RLA方法更易于解释。其代价是样本量更大。“基于不匹配的RLA”仅需对CVR差额设定下界，对于某些社会选择函数而言，这比计算特定错误的影响更容易。当总体不匹配率较低且CVR差额的下界接近真实CVR差额时，样本量的增加较小。然而，当错误包括那些若修正会扩大而非缩小CVR差额的错误、影响其他候选人（而非报告得票最少的胜出者与得票最多的落选者之间）差额的错误，或影响不同差额的错误时，样本量的增加可能非常大。