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Methods of Detecting Errors

Westcliff University

Telecommunication and Networking

Sanaz Tehrani


Methods of detecting errors

There are three main methods of detecting errors in a system; Single parity check, two-dimensional parity check, Checksum, and Cyclic redundancy check. Single parity is considered the simplest technique for detecting errors. It involves adding redundancy bits to a frame so that the total of each frame adds to an odd or even number. The most significant advantage of this approach is that it is easy to use it compared to other techniques. However, it may be challenging to detect two errors. For a single parity check, only an odd number is detected, having two odd numbers, therefore, results in no error.

A checksum refers to the sequence of letters and numbers that can detect errors in a file. Before detection, pieces of data are assigned some unique values when it has undergone a cryptographic function (Mahdavi et al., 2019). The unique values are a representation of the bits of the transmission. The value of an original file is used to detect any errors in its duplicate. This method is advantageous because it allows the detection of multiple errors in a single line. The method is also simple to apply. However, if a checksum id not as big as it should be, it may result in a collision and inaccurate results. According to research, a 16-bit checksum can detect up to 99.9 % of errors.

Cyclic redundancy Check, popularly known as CRC is used to check for accidental errors that may occur on raw data. Data that enters the CRC system have attached values and detects single errors, double bit errors, odd numbers, and specific burst errors. CRC is advantageous because it can detect more errors compared to other techniques. However, it is not easy to crack and the error must be solved without which it will be useless.


Mahdavi, M., Abedjan, Z., Castro Fernandez, R., Madden, S., Ouzzani, M., Stonebraker, M., & Tang, N. (2019). Raha. Proceedings of the 2019 International Conference on Management of Data – SIGMOD ’19. doi:10.1145/3299869.3324956

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