Measurement noise, other experimental errors, small sample size, cherry picking. It's worse than no data because you can draw incorrect conclusions. Have a rea

Measurement noise, other experimental errors, small sample size, cherry picking. It's worse than no data because you can draw incorrect conclusions. Have a really dumb example: All of the developers I work with are short, therefore all developers must be short, and we could save time by filtering job applicants by height. (Note: I work with only one developer. He's great!)