Statistical inference
Statistical inference deals with drawing conclusions about population parameters from an analysis of the sample data.

The two most important types of inferences are (1) estimation of parameter(s) and (2) testing of statistical hypotheses.

estimation of parameters
The true value of a parameter is an unknown constant that can be correctly ascertained only by an exhaustive study of the population, if indeed that were possible. Our objective may be to obtain a guess or an estimate of the unknown true value along with a determination of its accuracy. This type of inference is called estimation of parameters.
testing of statistical hypotheses
An alternative objective may be to examine whether the sample data support or contradict the investigator's conjecture about the true value of the parameter. This latter type of inference is called testing of statistical hypotheses.

Estimate a single value for the unknown (point estimation).

Determine an interval of plausible values for (interval estimation).
Hypothesis
Decide whether or not the mean height is 1.9 centimeters,which was previously found to be the mean height of a different stock of pine seedlings (testing a hypothesis).