Simplicity in Statistical Judgments

Simplicity in statistical judgments refers to choosing the explanation or model that is easiest to understand and use, without unnecessary complications. When faced with data, people tend to prefer simpler explanations that still fit the information well, because they are more straightforward to interpret and less likely to overfit random noise. This principle helps ensure that conclusions are clear, practical, and less prone to errors. In essence, simplicity acts as a guiding rule to balance accuracy with clarity, favoring models that are as simple as possible while still capturing the essential patterns in the data.