False positives

The idea of false positives is a very important statistical (data science) concept. A false positive is a mistake or an errored result. That is, it is a scenario where the results of a process or experiment indicate a fulfilled or true condition when, in fact, the condition is not true (not fulfilled). This situation is also referred to by some data scientists as a false alarm and is most easily understood by considering the idea of a recordset or statistical population (which we discussed earlier in this section) that is determined not only by the accuracy of the processing but by the characteristics of the sampled population. In other words, the data scientist has made errors during the statistical process, or the recordset is a population that does not have an appropriate sample (or characteristics) for what is being investigated.