Often phenomena that are important to understand and predict are very rare. Rare events can prove difficult to analyze systematically because they do not generate many sampling observations. In this article I examine how small sample sizes can be studied scientifically. The article begins with an explanation of the distinction between research and science. I then bring to the fore the importance of counterfactual comparisons and outline the nature of the methodological problems posed by the study of small samples. These problems include challenges related to using a single case, small sample sizes, selecting on the dependent variable, regression toward the mean, explaining a variable with a constant, and using the same data to both generate and test hypotheses. I provide potential resolutions to these problems which are: (a) employing matched controls; (b) shifting or widen the category of inquiry; (c) selecting variables based on variance in the independent variable; (d) including counterfactuals; (e) ensuring that both independent and dependent variables demonstrate variation; and (f) testing potential hypotheses against data sets that are fully independent of those used to generate the hypotheses. I conclude with a discussion of future directions for undertaking a more scientific approach to using small samples.

通常，对于理解和预测很重要的现象非常罕见。罕见事件可能难以系统地分析，因为它们不会产生很多抽样观察结果。在这篇文章中，我研究了如何科学地研究小样本量。这篇文章首先解释了研究与科学之间的区别。然后，我强调了反事实比较的重要性，并概述了小样本研究所带来的方法论问题的性质。这些问题包括与使用单个案例、小样本、选择因变量、回归均值、用常数解释变量以及使用相同数据生成和检验假设相关的挑战。我为这些问题提供了可能的解决方案：(a) 采用匹配控制；(b) 改变或扩大调查类别；(c) 根据自变量的方差选择变量；(d) 包括反事实；(e) 确保自变量和因变量均表现出变化；(f) 针对完全独立于用于生成假设的数据集的数据集测试潜在假设。最后，我讨论了采用更科学的方法来使用小样本的未来方向。(f) 针对完全独立于用于生成假设的数据集的数据集测试潜在假设。最后，我讨论了采用更科学的方法来使用小样本的未来方向。(f) 针对完全独立于用于生成假设的数据集的数据集测试潜在假设。最后，我讨论了采用更科学的方法来使用小样本的未来方向。