## Fine-print Examples

#### Of course it’s impossible list every example of engineers’ misuse of statistics but here are a few that come immediately to mind:

- Not understanding the difference between
*knowing*a parameter’s value and having to rely on an*estimate*of it. - Believing that
*p-values*measure the probability a null hypothesis. - Over-fitting: Trying to achieve a better description of the existing data with a more complicated model at the expense predicting future observations.
- Acting as though
*“the variance of a sum is the sum of the variances”*is*always*true. (It’s not.) - Not understanding that laboratory measurements are only
*one*realization. Another collection of nominally identical measurements would be different. - Thinking that repeated inspections improve reliability.
- Believing in \(R^2\).
- Not understanding the distinction between a
*valid*statistical statement and a*true*statistical statement. - Beliving that zero correlation means no relationship between two variables.
- Looking for reasons why your conclusion is correct rather than for reasons it
*might possibly*be wrong.*****

***** In fairness this is more an engineer’s engineering failing than a statistical one.