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. 2020 Jan 17;11:358. doi: 10.1038/s41467-019-14203-0

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 9 — a and b show the effects of p-hacking on the number of experiments conducted and the number of effects discovered. p-hacking is operationalized by raising the type I error rate, $α = . 2$ . c and d show the effects of keeping $α = . 05$ but adding up to five batches of additional participants (each batch containing 1, 5, or 10 participants), if an effect failed to reach significance. Results are shown averaged across levels of temperature.