[3][4] The use of a P value cut-off point of 0.05 was introduced by R.A. Fisher; this led to study results being described as either statistically significant or non-significant.
[5] Although this p-value objectified research outcome, using it as a rigid cut off point can have potentially serious consequences: (i) clinically important differences observed in studies might be statistically non-significant (a type II error, or false negative result) and therefore be unfairly ignored; this often is a result of having a small number of subjects studied; (ii) even the smallest difference in measurements can be proved statistically significant by increasing the number of subjects in a study.
However, using patient-reported outcomes does not solve the problem of small differences being statistically significant but possibly clinically irrelevant.
MCID therefore offers a threshold above which outcome is experienced as relevant by the patient; this avoids the problem of mere statistical significance.
Schunemann and Guyatt recommended minimally important difference (MID) to remove the "focus on 'clinical' interpretations" (2005, p. 594).
This method allows for more personal variation, as one patient might require more pain relief, where another strives towards more functional improvement.
High post treatment satisfaction results in insufficient discriminative ability for calculation of a MID.
[13] MID calculation is of limited additional value for treatments that show effects only in the long run, e.g. tightly regulated blood glucose in the case of diabetes might cause discomfort because of the accompanying hypoglycemia (low blood sugar) and the perceived quality of life might actually decrease; however, regulation reduces severe long term complications and is therefore still warranted.
For example, use of the standard error of the mean (SEM) is based on anecdotal observations that it is approximately equal to 1/2 SD when the reliability is 0.75.