Community science—scientific investigation conducted partly or entirely by non-professional scientists—has many advantages. For example, community science mobilizes large numbers of volunteers who can, at low cost, collect more data than traditional teams of professional scientists. Participation in research can also increase volunteers’ knowledge about and appreciation of science. At the same time, there are worries about the quality of data that community science projects produce. Can the work of non-professionals really deliver trustworthy results? Attempts to answer this question generally compare data collected by volunteers to data collected by professional scientists. When volunteer data is more variable or less accurate than professionally collected data, then the community science project is judged to be inferior to traditional science. I argue that this is not the right standard to use when evaluating community science, because it relies on a false assumption about the aims of science. I show that if we adopt the view that science has diverse aims which are often in tension with one another, then we cannot justify holding community science data to an expert accuracy standard. Instead, we should evaluate the quality of community science data based on its adequacy-for-purpose.

社区科学——部分或全部由非专业科学家进行的科学研究——具有许多优势。例如，社区科学动员了大量志愿者，他们能够以低成本收集比传统的专业科学家团队更多的数据。参与研究还可以增加志愿者对科学的认识和欣赏。与此同时，人们对社区科学项目产生的数据质量感到担忧。非专业人士的工作真的能带来值得信赖的结果吗？回答这个问题的尝试通常将志愿者收集的数据与专业科学家收集的数据进行比较。当志愿者数据比专业收集的数据更多变或更不准确时，社区科学项目被判断为不如传统科学。我认为这不是评估社区科学时使用的正确标准，因为它依赖于对科学目标的错误假设。我表明，如果我们认为科学有不同的目标，而这些目标往往相互矛盾，那么我们就无法证明将社区科学数据保持在专家准确性标准是合理的。相反，我们应该根据其充分性来评估社区科学数据的质量。