Abstract
Data obtained from multiple observations for the same object generally take different values. Normally, the differences within a dataset are regarded to be caused by some errors. The errors are usually assumed to be the normal distribution. Then, the average value AV and standard deviation SD are calculated performed from the dataset. On the other hand, Oda (1992) proposed the new indicator C .. of the sum of contradictions of multiple observed values. The idea for the new indicator is that the irrelevancy-contradiction (C) index may be used for estimating psychological feelings to the distribution of multiple observations better than traditional indexes like SD values. C.. is the sum of C values. C .. is an extended form of the so-called "Range" index, and takes different function forms for the observation number. In the function forms of C.., the linearly ascending weight values are multiplied to the internal Range values. Also, if n is an odd number, the median data is not used. Furthermore, we obtain the ambiguity index AMB=2*(C../n^2), so that it can take almost near values of the SD values obtained from the same dataset. In this research, in order to reveal the characteristics of the AMB index for the small n values, we conducted the Monte Carlo Simulation for the purpose of clarifying the zone in the multiple dimensional space, where data with large differences between the SD and the AMB value.
