The Number of Identified Specimens or Number of Individual Specimens (NISP) is defined as the number of identified specimens for a specific site or skeleton. And a good NISP is a very desirable goal. The number of individual specimens is the most important factor in calculating a NISP. When it comes to this project, there are three types of identification involved in this project. It is from the remains of humans, animals, and plants. In this regard, a lot of work has been carried out in the United States and Germany by the National Academy of Sciences to produce a standard classification. Some believe that the NISP is just a basic classification of a site or skeleton and there are many ways to calculate it. NISP is a basic technique that is widely used for estimating the relative abundance of specimens in a collection Discussing cutting is a common practice in cutting-edge archaeology. However, there may currently be no consensus on a first-class way to quantify them due to many problems at the archaeology sites. However, archeologists can compute experimental methods to evaluate manipulative streak patterns with ten very fragmented simulated forelimbs and hindlimbs, with reduced use of NISP due to quantification units and footprint. In addition, the frequencies of the ordinal scale of the anatomical parts of the crest (proximal, marginal, distal) are not constant and fluctuate in simulators. The paleontological analytical results show the significant differences between the two quantitative methods. An alternative estimate to NISP, often done in concert, is minimum number of individuals (MNI). Both are influenced by fragmentation and limited preservation, but in different ways. NISP tends to overestimate the number of individuals under moderate fragmentation, but the overestimate lessens as fragmentation increases due to the inability to classify the bones. MNI tends to underestimate the actual number under medium fragmentation, and even more severely when bones are highly fragmented. Under hypothetically perfect preservation and no fragmentation, these estimates should be the same. MNI also suffers from the aggregation problem, in which different aggregations will generate at least two values, a MNI minimum and maximum, which are generally empirically indistinguishable. Both NISP and MNI are likely only ordinals scale measurements, which means at best they can only give an ordered series of taxonomic abundance, i.e. "Taxon A is more numerous than Taxon B." NISP should not be used when calculating a sample size for inferential