The term is used in the field of statistics and probabilities and is applied to those data or figures that should be the same but that nevertheless are not. They receive this denomination because they represent the same and must be interpreted in the same way but are calculated with different methods or they do not come from the same source, being taken with different collection techniques.
This denomination is used to refer to data, generally, indicators or indices that come from the calculation with statistical methods, using a large number of samples. These data represent the same phenomenon, for which they are calculated, however the method used for their determination is different, as long as they are valid, that is, it is possible to arrive at said data in both ways.
Another way that results in a statistical discrepancy between two numbers or data is that the sources of information or data collection techniques are different or that they represent variations. It should be noted that statistics is a science that allows inferences to be made towards entire data populations, based on a representative sample, but it is not precise, therefore a term called error or slack range is also used in this science.
Based on the aforementioned, it can be said that a statistical discrepancy is technically acceptable when the values are within the error range.
Uno de los casos más comunes donde se presenta la discrepancia estadística es en la economía, donde al calcular el producto interno bruto de un país se realiza con nubes de datos recolectados de forma independiente según criterio industrial o de consumo.
