Abstract
Statistical inference provides the protocols for conducting rigorous science, but data plots provide the opportunity to discover the unexpected. These disparate endeavours are bridged by visual inference, where a lineup protocol can be employed for statistical testing. Human observers are needed to assess the lineups, typically using a crowd-sourcing service. This paper describes a new approach for computing statistical significance associated with the results from applying a lineup protocol. It utilizes a Dirichlet distribution to accommodate different levels of visual interest in individual null panels. The suggested procedures facilitate statistical inference for a broader range of data problems.
Citation
[1] S. Vanderplas, C. Röttger, D. Cook, et al. “Statistical significance calculations for scenarios in visual inference”. In: Stat 10.1 (Dec. 01, 2021), p. e337. DOI: https://doi.org/10.1002/sta4.337.
@article{stat2020,
author = {Susan Vanderplas and Christian R{\"o}ttger and Dianne Cook and Heike Hofmann},
title = {Statistical significance calculations for scenarios in visual inference},
journal = {Stat},
publisher = {Wiley},
volume = {10},
number = {1},
pages = {e337},
doi = {https://doi.org/10.1002/sta4.337},
year = {2021},
month = {dec},
}