This research proposes the variance estimation method of Pareto data and to compare three variance estimation methods. Those methods are simple method the adjusted variance method with term (n - 1)/n and the adjusted variance method with term (n - 2)/n. The research was considered by the absolute of bias (|Bias|) the mean absolute errors (MAE) and the mean square errors (MSE) of variance estimator. This research used Monte Carlo Simulation method. Results of this research show that the |Bias| of simple method is the lowest for almost all conditions. We can arrange the methods as follows: the adjusted variance method with term (n - 1)/n and the adjusted variance method with term (n - 2)/n respectively. For all situations the MAE and MSE the of adjusted variance method with term (n - 2)/n is the lowest. We can arrange the methods as follows: the adjusted variance method with term (n - 1)/n and simple method respectively.

The objectives of this research are to propose the mode estimation method of Inverse Gaussian data and to compare three mode estimation methods. Those methods are simple method adjusted mode method with term (n+1)/n and adjusted mode method with term (n+2)/n. The performance of each method can be measured by the absolute bias (|Bias|) and the mean square errors (MSE). This study was performed by using different sample sizes (n) of 5 10 20 30 and 50 whereas the parameter  is fixed to be 1 and parameters  are fixed to be 1 3 5 10 15 and 20. This research used the Monte Carlo Simulation technique. The experiment was repeated 50000 times for each condition. The results showed that the adjusted mode method with term (n+2)/n gave the lowest value of |Bias| and MSE in most of the criterions of sample sizes and parameter values.