The expected value of a random variable, in a simple sense, is nothing but the arithmetic mean.
Exact tests are hypothesis tests that are guaranteed to produce Type-I error at or below the nominal alpha level of the test when conducted on samples drawn from a null model.
In statistical models, error or residual is the deviation of the estimated quantity from its true value: the greater the deviation, the greater the error.
Endogenous variables in causal modeling are the variables with causal links (arrows) leading to them from other variables in the model.
In a study or experiment with two groups (usually control and treatment), the investigator typically has in mind the magnitude of the difference between the two groups that he or she wants to be able to detect in a hypothesis test.