The commonest mistake
October 13, 2019•50 words
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.