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[en] Highlights: • Sensitivity of four wind speed distributions functions are investigated. • Two sets of wind data consist of the actual and truncated data are created. • MOM and MLE are used to estimate the parameters used in the distribution functions. • Nine goodness-of-fit indices are examined to find best fit function. • The most effective distribution model is the Weibull and MLE estimator for the truncated data. - Abstract: Accuracy of wind speed data has important impact on determining wind power output from a wind turbine. There are many researches on four widely used wind speed distribution models described by gamma, lognormal, Rayleigh and Weibull for assessing wind potentials. However, there is lack of studies to evaluate sensitivity of these models with respect to accuracy of the measured wind data. In this paper, wind speed data are obtained from SUNA (renewable energy organization of Iran) for two years period from 2014 to 2016 in 10 min time intervals, for five stations in the province of Kerman in Iran. The maximum-likelihood estimator (MLE) and method of moments (MOM) are used for calculating parameters involved with these four distribution functions. For sensitivity analysis, a truncated set of wind data is generated by removing the decimal digits of the wind data; reducing the resolution to 1 m/s. The best fit functions to actual and truncated wind speed data are selected by examining nine goodness-of-fit statistics. From the results, it is observed that the lognormal function gives a better fit to the actual data, while the Weibull model performs better using the truncated wind speed data. The Rayleigh distribution does not fit well for both data types. The MOM method has performed better for calculating the parameters of the gamma distribution while the MLE is the preferred method for obtaining the parameters of the Weibull function particularly when working with truncated wind data.