The commonest mistake

While statistical significance often is mistaken as an indication of practical importance or scientific relevance, an even greater mistake is to believe that statistical non-significance indicates equivalence or "no difference". It doesn't. Statistical non-significance reflects uncertainty, which perhaps can be considered as an indication of a too small sample size.

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