When the clinician gets the figures of body measurements or laboratory and functional tests, he has to see if the results fall into the range of normal variability or outside. This may only be defined by reference to an alleged homogeneous population whose description implies statistical tools in order to specify the distribution of clinical features. In clinical-therapeutical research like clinical trials, a more elaborate knowledge of the kind of distributions of the characters under study is needed. Very often, for the validity of the statistical calculations, the raw data will not be used as such and appropriate transformations of the data will be used.After recalling the importance of distribution functions in clinical research, the author gives the 1—99 or 2,5—97,5 percentile method of assessing the cases normally belonging to the population. The scale of coefficient of variation of clinical measurements goes from 0,5 % for the basal temperature to 65 % for the minimal effective dosis of digitalics.To ensure the legality of statistical reduction of data and analysis, such as analysis of variance, it is often necessary to perform transformations of raw data. Some of these lead to useful graphical representations on special papers.For continuous variables such as height, the author considers the probit transfpr-mation of percentages. The r ankit is used in the case of ordinal data. The logarithmic transformation of data such as body weight, survival time in leukaemia and other cancers, gives straight lines after probit transformation of the cumulated percentages.For discontinuous variables or bi nomial variab 1 e s such as counts of improved patients by several treatments, the angular transformation gives a clear graphical representation of the results of clinical trials (binomial probability paper).In biological assay, probits and logits are widely used. For Poisson variables like Thoma’s chamber and radioactive counts, the root square transformation is appropriate. A Poisson-sum probability paper allows a quick graphic test of the Poisson distribution.Finally, for recurrent events such as the RR interval in ECG of complete arrhythmia by auricular fibrillation, the concept of imminence (MACREZ) of occurrence of a further systole involves a simple transformation of the frequency function of the RR interval and allows an easier dissection of this frequency function in several components.In conclusion the author shows the necessity for the research clinician to have a good training in mathematics, statistics, sampling theory and practice and emphasizes the interest of collaboration amongst clinicians both at national and international levels.