I can see clearly now: reinterpreting statistical significance

TL;DR

This paper reinterprets null hypothesis significance testing as a measure of statistical clarity, proposing a semantic shift to improve communication and reduce misuse in scientific research.

Contribution

It introduces the idea of describing null hypothesis test results as 'clarity' rather than 'significance' to enhance understanding.

Findings

Reinterpreting significance as clarity improves communication.

The semantic change can reduce misinterpretation of statistical results.

Null hypothesis tests remain useful when framed as measures of clarity.

Abstract

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.