Rewriting results sections in the language of evidence.

Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P -value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the 'good practices' that exist in other fields. It has been known for decades that there are severe problems associated with null-hypothesis significance testing (NHST) based on arbitrary P -value thresholds (e.g., P = 0.05). A small literature review indicates that much of the current research in ecology and evolution is still disregarding the warnings and frequently relies on binary decisions based on P -values to report statistical significance. While the P -value itself is a sound mathematical concept that does not have to be banned when used correctly, we should stop using the term 'statistical significance' and replace it with a gradual notion of evidence. Language matters and 'evidence' is an intuitive concept that honestly reflects what the data really tell us. To facilitate rewriting scientific results, we offer generic examples of how to translate (binary) significance language into a gradual language of evidence. [ABSTRACT FROM AUTHOR]