## 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.