In
statistics,
interval estimation is the use of
sample data to calculate an
interval of possible (or probable) values of an unknown
population parameter, in contrast to
point estimation, which is a single number.
Jerzy Neyman (1937) identified interval estimation ("estimation by interval") as distinct from point estimation ("estimation by unique estimate"). In doing so, he recognised that then-recent work quoting results in the form of an
estimate plus-or-minus a
standard deviation indicated that interval estimation was actually the problem
statisticians really had in mind.