10 Statistical Traps We Often Overlook

Understanding data requires looking beyond simple calculations and formulas. The 'average' can be misleading due to outliers, making the median a better measure in some cases, while a single number hides the full story of a dataset's distribution. It is critical to question observed relationships, as correlation does not imply causation and confounding variables are often present. Other common pitfalls include confirmation bias, misinterpreting graphs by not scrutinizing their axes, and confusing statistical significance with real-world practical importance.