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Do We Really Need the S-word?

The use of “significance” in reporting statistical results is fraught with problems—but they could be solved with a simple change in practice

Megan D. Higgs

Click to Enlarge ImageIt pervades journal articles, media reports and discussions of nearly all quantitative, data-based research. It is so entwined with statistical inference that we subconsciously think it, say it and write it without stepping back to reflect on its intended meaning. It attaches an air of importance (or lack thereof) to results often not worthy of the label. Significance, the new s-word, is overused and underdefined in the realm of connecting statistical results to the underlying science.

The current Wikipedia entry for “statistical significance” clearly distinguishes between the word’s statistical and common meanings, stating, “When used in statistics, significant does not mean important or meaningful, as it does in everyday speech.” However, I believe we are unable (or perhaps too untrained) to set aside the lay meaning of the word when reading it in the context of statistical results. For scientists, statisticians, journalists and others who write or speak about statistical results, I advocate a simple solution: Replace the s-word with words describing what you actually mean by it.




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