We examine features of citizen science that influence data quality, inferential power, and usefulness in ecology. Background context includes ecological sampling (probability-based, purposive, opportunistic), linkage between sampling technique and statistical inference (design-based, model-based), and scientific paradigms (confirmatory, exploratory). We distinguish several types of investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking sampling design, and examine overarching objectives, design, analysis, volunteer training and performance. Projects with strong designs, expertise and training of volunteers, and professional oversight are well suited for ecological research objectives, and can produce high-quality data with strong inferential power. Projects with little or no sampling design and minimal volunteer training are better suited for general objectives related to public education or data exploration, as reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods and/or external data may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. Citizen science can potentially make valuable contributions to conservation by increasing the scope of species monitoring efforts with standardized protocols, state-of-the-art analytical methodology, and well-supervised programs. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to ensure the data quality required, and including necessary statistical expertise, thereby strengthening the "science" aspect of citizen science and enhancing acceptance by the scientific community and decision makers. Article impact statement: If citizen science projects are designed specifically for the purpose, ecological hypothesis testing in the confirmatory paradigm is possible. This article is protected by copyright. All rights reserved.