Single case studies continue to play an important role in neuropsychological research. However, the range of statistical tools specifically designed for single cases is still limited. The current gold standard is the Crawford's t-test, but it is crucial to note that this is limited to simple designs and it is not possible to make inferences relevant to support for the null hypothesis with it. The Bayesian Multilevel Single Case models (BMSC) provide a novel tool that grants the flexibility of linear mixed model designs. BMSC is also able to support both null and alternative hypotheses in complex experimental designs using the Bayesian framework. We compared the BMSC and Crawford's t-test in a simulation study involving a case of no-dissociation and a case of simple dissociation between a single case patient and a series of control groups of different sizes (N = 5, 15, or 30). We then showed how BMSC is useful in complex designs by means of an example using real data. The BMSC proved to be more reliable than the Crawford's test, in terms of first-type errors and more precise estimating the parameters. Notably, the BMSC model provides a comprehensive vision of the whole experimental design, interpolating a single model. It follows the recent trend which involves a shift in attention from p-values to other inferential indices and estimates.

单个案例研究继续在神经心理学研究中发挥重要作用。但是，专门为单个案例设计的统计工具的范围仍然有限。当前的黄金标准是Crawford的t检验，但必须指出的是，这仅限于简单的设计，并且不可能用推论来支持原假设。贝叶斯多级单案例模型（BMSC）提供了一种新颖的工具，可为线性混合模型设计提供灵活性。BMSC还能够使用贝叶斯框架在复杂的实验设计中支持零假设和替代假设。我们比较了BMSC和克劳福德的牛逼在一个模拟研究中进行了一项检验，该研究涉及一个案例患者和一系列不同大小的对照组（N = 5、15或30）之间没有解离和简单解离的情况。然后，我们将通过一个使用真实数据的示例展示BMSC在复杂设计中的有用性。在第一类错误和更精确的参数估计方面，BMSC被证明比Crawford的测试更可靠。值得注意的是，BMSC模型提供了整个实验设计的全面视野，并插值了单个模型。它遵循了最近的趋势，该趋势涉及将注意力从p值转移到其他推论性索引和估计。