Error

Definition of Error

Error: An error is an incorrect result produced by a calculation. In data science, an error is an inconsistency or inaccuracy in data. Errors can be the result of incorrect measurements, incorrect entry of data, or simply a mistake. In order to ensure the accuracy of data, it is important to identify and correct any errors that may be present.

What is an Error used for?

An Error is an indicator of the difference between the expected outcome and actual result of a process. It is used to measure the accuracy, reliability, and validity of a given system or algorithm. Error can be classified into two types: type I error and type II error.

Type I error (also known as false-positive) occurs when a system incorrectly identifies something as positive when it is actually negative, which means rejecting a null hypothesis when it should have been accepted. Type I errors are usually associated with high sensitivity levels in algorithms or tests.

On the other hand, type II error (also known as false-negative) occurs when a system fails to identify something as positive when it was actually positive, meaning accepting a null hypothesis when it should have been rejected. In contrast to type I errors, type II errors are usually associated with low sensitivity levels in algorithms or tests.

Therefore, errors can play an important role in decision making by providing insight into the potential accuracy of a particular system or algorithm before any significant investments are made. Additionally, they can also help inform future improvements by highlighting areas that need to be further developed or tweaked.