Introduction: The "Quality Control" of AI
Many beginners believe that building better AI means using more complex algorithms. However, 80% of model errors stem from not understanding the data distribution, not the algorithm itself.
If you feed a model garbage, it will predict garbage with high confidence. Statistics is not just a math prerequisite; it is the quality control engine for Machine Learning. It allows you to verify assumptions before writing a single line of modeling code.
How do we summarize massive datasets to detect hidden patterns?
Answer: We use measures of central tendency and dispersion to understand where the data is located and how spread out it is. Before you apply an algorithm, you must understand the "shape" of your data.