Clinical trials provide a practicing clinician with an abundance if not excess of data to draw on when comparing treatment methods and deciding the potentially optimal therapeutic option for a given patient. NNT, or number needed to treat, has been identified as one of the parameters useful for assessing the effectiveness of therapy. It represents the number of patients who must undergo a given health-care intervention instead of another one to see a difference in the effectiveness of obtaining a desired outcome within a set timeframe. NNT is a derivative of absolute risk reduction (ARR) or initial risk and its relative reduction. It represents the relative superiority of a given treatment. NNT is primarily used for comparing the advantages and disadvantages of alternative health-care interventions, and its assessment is important for estimating the clinical value of statistically significant clinical trial results. Utilization of NNT allows to predict therapy outcome both in terms of its effectiveness and tolerance. Also, clinical trial results presented in the form of NNT may be easily shared with patients, their families, and the institutions deciding the availability of a given drug. In multiple sclerosis, clinical trial results have been concerned with the impact of therapeutics on decrease of annualized relapse rate (ARR) and reduction of lesions visible in magnetic resonance images as well as slowing of disability progression. Analyses of first-line multiple sclerosis treatments reveal their NNT referred to prevention of relapses, disability progression and lesions in magnetic resonance image to vary significantly. Similar differences exist across NNT values established for second-line treatments or the oral therapies being currently introduced. The data clearly show that when evaluating clinical trial results, it is not enough to consider only given parameters, as they must all be critically and constructively analysed. NNT’s importance is also stressed as a clear parameter to be used for the evaluation of economic outcomes in healthcare.