Big Data Fallacy. The law of large numbers argues that the sample’s mean approaches the sample population’s actual average as a sample size increases. This concept is often, either implicitly or explicitly, taken as a justification as to why ‘big data’ (i.e., millions or billions of samples) cannot be wrong. However, there are contrary arguments and evidence. The big data fallacy implies that more data does not translate to more information in equal measure.
The implication is that if an error occurs in a small sample of data, making the sample ‘big’ does not mystically eradicate this error.