Garbage in, garbage out (GIGO)
"Garbage in, garbage out" (GIGO) is a principle in computer programming and mathematics emphasizing that the quality of output is directly linked to the quality of input. This concept asserts that if a system receives flawed or incomplete data, the results produced will also be flawed, regardless of the system's sophistication. The term is often associated with early computing, with origins tracing back to references made by Charles Babbage in the 1800s regarding his Difference Engine, which highlighted the importance of accurate data.
The phrase became more widely recognized in the mid-20th century, potentially arising from military slang or accounting terminology. It serves as a reminder that computers and algorithms do not possess independent reasoning; they operate based solely on the data provided. As a result, whether in statistical analysis or business modeling, ensuring the integrity and relevancy of input data is crucial for achieving reliable outcomes. For instance, using outdated demographic data can lead to misguided conclusions in market analyses. Overall, the GIGO principle underscores the significance of data quality in achieving meaningful results across various applications.
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Garbage in, garbage out (GIGO)
Garbage in, garbage out (GIGO) is an informal term used in computer programming and mathematics. It refers to the concept that the quality of the output from an information system is only as good as the quality of the input. For example, a computer program that receives incomplete or poor-quality information is unlikely to yield high-quality results, no matter how accurate the program. Similarly, in mathematics, an equation with incorrect starting data will result in an inaccurate solution.
Overview
The origin of the phrase "garbage in, garbage out" is debated, but it is universally used to remind people that a machine is only as accurate as the data it receives. An early reference to the idea is sometimes credited to eighteenth-century British mathematician and engineer Charles Babbage. In the 1820s, Babbage invented a primitive computing device he called the Difference Engine. The engine was not a computer in the modern sense but a mechanized calculating device. According to his writings, Babbage was once asked if his device would produce the right answer even if the wrong figures were put into it. He was surprised that anyone would even think such a result was possible.
A newspaper article from 1957 suggests the phrase "garbage in, garbage out" was military slang used by engineers working with primitive vacuum tube computers. Others speculate it evolved from the accounting phrases "last in, first out" (LIFO) and "first in, first out" (FIFO). The terms had been used to refer to managing stocks and company assets since the 1930s.
Many sources credit IBM programmer George Fuechsel with coining the phrase. Fuechsel said he used it while teaching computer courses in 1958 or 1959. In a 1963 story, a news reporter for the Associated Press wrote that he heard computer programmers at an Internal Revenue Service (IRS) processing center use the term.
While more advanced computers are capable of increasingly complex functions, most computers do not think for themselves; they simply follow the instructions laid out in their programming. Data input is received by a computer's central processing unit (CPU), sometimes called the "brain" of a computer. The CPU interprets and executes the instructions it receives from the computer's hardware and software programming. The CPU takes the input, calculates it according to its programming, and produces information as output. If the CPU receives "garbage" input, such as error-riddled data or data from the wrong software, the output information will not be reliable.
The same concept applies to data used to develop a statistical analysis or a business model. Both formats use data to create a statistical picture that is meant to represent the real world. No matter how reliable the program, if the input data is poor, the result will not be accurate. For example, demographic data that is too old may not reflect the modern business landscape. Also, information gathered from too narrow of a source, such as political polling data taken from a highly partisan area, will not give accurate results of the overall picture.
Bibliography
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