Wildlife disease surveillance helps in protecting public health, agriculture, and biodiversity. Planning effective surveillance involves strategic methods for identifying an effective sampling design for a program’s objectives. Gaps in existing standards and complexity for wildlife surveillance justify a need for tools that can build statistically-based intuition in wildlife professionals when designing surveillance systems. To address this need, we present the use of plug-and-play tools, specifically our Surveillance Analysis and Sample Size Explorer (SASSE), to allow wildlife professionals to build intuition about the role of sample size vis-a-vis sampling design and diagnostic test performance in wildlife systems. SASSE uses open-source software (R, R Shiny) to design an interactive, module-based teaching tool to cover key surveillance objectives including detection, prevalence, and epidemiological dynamics. Our tool fills the following gaps: 1) allows a broad audience to apply statistical sample size theory for designing disease surveillance, and 2) provides a simple statistical tool for addressing challenges with disease surveillance design in wildlife populations. Thus, the tool we present here can be used readily to identify efficient sampling designs for a surveillance objective across a wide variety of settings.