"Garbage in, garbage out" (GIGO) is a concept in information science and computers that suggests the quality of output is determined by the quality of the input. It implies that if you provide incorrect or faulty data as input, the system will inevitably produce poor, inaccurate or misleading results as output.
Garbage In, Garbage Out in practice
The GIGO principle works on the assumption that the input and output are directly related. If the data entered into a system is flawed (the "garbage in"), the results or decisions based on that data will also be flawed (the "garbage out").
For instance, if you have a financial forecasting model and you feed it inaccurate historical data, the predictions it derives will be distorted, hence untrustworthy. This principle applies to any system where data is processed, be it software programs, machine learning algorithms, or decision-making processes.
It is thus important for users to ensure that the data they provide is as accurate and high-quality as possible to ensure useful and reliable results. The principle is often used to emphasize the importance of thorough validation and cleaning processes in data handling.
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