In wireless sensor networks (WSN) localization of the nodes is relevant, especially for the task of identification of events that occur in the environment being monitored. Thus, positioning of the sensors is essential to satisfy such task. In WSN, sensors use techniques for self-localization based on some reference or anchor nodes (AN) that know their own position in advance. These ANs are fusion centers or nodes with more processing power. Assuming that the number of ANs given in the network is N, we carry out the localization algorithm to position sensors sequentially using those N ANs. Now, when a sensor has been localized, it becomes a new AN, and now, other sensors will use N+1 ANs, this is repeated until all the sensors in the network have been localized. In this sequential localization algorithm, the positioning error (difference between true and estimated position) increases as the sensor to be located is farther away from the group of original ANs in the network. This error becomes critical when propagation issues such as mutlipath propagation and shadowing in indoor environments are considered. In this paper, we characterize statistically positioning error in WSN for one and two-dimensional indoor environments when sensors are deployed randomly with different distributions. We also evaluate the performance of the localization algorithm and determine correcting factors based on the statistical characterization to minimze positioning error. We present results from simulations and measurements in an indoor environment.