This answer is framed in terms of particle physics, but the basic idea can be generalized to any analysis where the presence or absence of the thing you are trying to study must be deduced from the raw data.
Many particle physics detectors collect data in a very general sense, and to study something in particular you need to select from all the myriad recorded events that those that correspond to the physics you want to study.
That is, you are going to partition the whole data set into those events that you believe represent your physics and those that do not. Obviously you can make two classes of errors in that partition:1
- You can include events in your study group that represent some physics other that what you wanted (false positive)
- You can leave some even that does represent your physics out of your study group (false negative)
The term signal efficiency is usually defined as the fraction of the desired events that you actually get (i.e. it goes down as the false negative rate goes up). The term background rejection is usually defined as the fraction of events that don't belong in your sample that are excluded from your sample (i.e. it goes down as the false positive rate goes up).
1 And it is generally the case that the harder you work to prevent one class of errors the more of the other class you are going to get, so there is a trade-off here.