I can see clearly now: Reinterpreting statistical significance

Null hypothesis significance testing (NHST) remains popular despite decades of concern about misuse and misinterpretation. There are many recent suggestions for mitigating problems arising from NHST, including calls for abandoning NHST in favour of Bayesian or information-theoretic approaches. We believe that NHST will continue to be widely used, and can be most usefully interpreted as a guide to whether a certain effect can be seen clearly in a particular context (e.g. whether we can clearly see that a correlation or between-group difference is positive or negative). We believe that much misinterpretation of NHST is due to language: significance testing has little to do with other meanings of the word 'significance'. We therefore suggest that researchers describe the conclusions of null-hypothesis tests in terms of statistical 'clarity' rather than 'significance'. We illustrate our point by rewriting common misinterpretations of the meaning of statistical tests found in the literature using the language of 'clarity'. The meaning of statistical tests become easier to interpret and explain when viewed through the lens of 'statistical clarity'. Our suggestion is mild, but practical: this simple semantic change could enhance clarity in statistical communication.