  Null hypothesis significance testing remains popular despite decades of
concern about misuse and misinterpretation. We believe that much of the problem
is due to language: significance testing has little to do with other meanings
of the word "significance". Despite the limitations of null-hypothesis tests,
we argue here that they remain useful in many contexts as a guide to whether a
certain effect can be seen clearly in that context (e.g. whether we can clearly
see that a correlation or between-group difference is positive or negative). We
therefore suggest that researchers describe the conclusions of null-hypothesis
tests in terms of statistical "clarity" rather than statistical "significance".
This simple semantic change could substantially enhance clarity in statistical
communication.
