Abstract <p>Formulating problems of statistical analysis of data with a gamma distribution belongs to the classical mathematical statistics domain. Oddly enough, not all problems have been solved within the framework of parametric statistics. These gaps should be bridged, since the gamma distribution is widely used in theoretical and applied research. A plausible approach is provided by the National Standard GOST 11.011–83 Applied statistics. Rules to determine estimates and confidence limits for gamma distribution parameters. The standard gamma distribution is determined by the shape parameter. When switching to the scale-shift family, the scale and shift parameters should be added. Seven formulations of parameter estimation problems are explored, since each of the three parameters can be either unknown or known. For each of the formulations, the estimates of the method of moments and their asymptotic variances are found. The maximum likelihood estimates are obtained for a known shift parameter. Single-step estimates, which are asymptotically equivalent to the maximum likelihood estimates, are used for an unknown shift parameter. The presence of measurement errors affects the accuracy of parameter estimates when certain calculation algorithms are applied. Based on the interval data model, GOST 11.011–83 provides the rules to choose the estimation method for unknown shape and scale parameters when the shift parameter is known. At the stage of developing GOST 11.011–83, problems were identified that required a new approach. The new scientific results obtained in solving practical problems resulted in finding new areas for research. Among those are the statistics of interval data, as well as single-step estimates. The statistics of interval data as a branch of mathematical statistics has reached maturity. It covers all principal areas of statistical methods. It is an important part of systemic fuzzy interval mathematics.</p>