Single well test analysis, is a type of aquifer test which try to estimate Aquifer’s hydro-dynamic properties merely using the drawdown data which is collected directly from the pumping well.Beside the reviewing analytical methods in this study, the inverse numerical method has been applied to estimate hydro-dynamic parameters of synthetic and real time-drawdown data collecting from 28 aquifer test performed alongside the ‘Khorasan Razavi’ province. Results concluded that the inverse method is effective and reliable for interpretation of hydro-dynamic parameters from normal time-drawdown data and data gathered from single pumping tests. Different analytical methods has applied to estimate Well loss and aquifer loss parameters and the results are compared to the reference data, calculated using piezometer collected drawdowns.  Based on the results, dependence of well losses to the time and its sensitivity to the observed data altogether with the dependency of results to the user’s graphical judgments are the main limitations of conducted study to properly analyzing single well data.  The results also indicate that Karami and Younger’s Method could give a direct and rather good evaluation of well loss for correcting time-drawdown data gathered from the pumping well. By applying this method calculated parameters are dependent to the selected time frame and there is not necessarily tend to an acceptable estimation of well losses on the data consolidation phase. Though, the Expert judgments could be an acceptable base in data range selection. In some of the analyzed tests, Time-Drawdown data has a rough time dependency and correction should perform based on a variable well loss estimation.For this purpose, a modified method based on the Karami& Younger’s Approach has been performed on a semi-log chart. We concluded that applying modified method on several Pumping test data could be tend to an acceptable and direct evaluation of well losses appropriate to time (CQn=f(t)) and hydrodynamic parameters directly. However the results are sensitive to the data grouping based on the selected time frames and data density.