Tag: Data Analysis
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GIGO – Garbage In, Garbage Out
The principle “Garbage In, Garbage Out” (GIGO) asserts the essential link between input data quality and output reliability, emphasizing the need for careful data validation. Rooted in computing history, its relevance spans across fields, advocating for meticulous data handling to ensure accurate outcomes.
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Goodhart’s Law
Coined by Charles Goodhart, the principle “When a measure becomes a target, it ceases to be a good measure” highlights the unintended repercussions of emphasizing a singular metric. Originating from monetary policy observations, the principle reveals how entities adjust their behaviors in response to metrics becoming primary objectives across diverse sectors.
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Expected Value
Expected value, a cornerstone of statistics and probability, indicates the average outcome of repeated events. Despite its ubiquity in fields such as economics and decision-making, it doesn’t predict individual outcomes and can be skewed by outliers. Its broad applications necessitate considering ethical implications due to potential unequal impacts.
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Type 1 and Type 2 Errors
In statistics, Type 1 and Type 2 errors relate to inaccurate conclusions in tests. Type 1 is a false positive, rejecting a true idea, while Type 2 is a false negative, accepting a false idea. Balancing these errors is essential for valid study results.
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Pure vs. Applied Research
Pure research seeks to expand fundamental knowledge, driven by curiosity, while applied research is designed to solve practical problems with immediate applications. Both are crucial for knowledge advancement and interact to inform each other.
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Signal-to-Noise Ratio
The signal-to-noise ratio (SNR) measures the level of desired information (signal) against irrelevant or distracting information (noise). A high SNR means clearer content, while a low SNR implies more distraction. This concept applies in areas like data communication and everyday conversation.