Statistical Test Theory
In statistical test theory the notion of statistical error is an integral part of hypothesis testing. The test requires an unambiguous statement of a null hypothesis, which usually corresponds to a default "state of nature", for example "this person is healthy", "this accused is not guilty" or "this product is not broken". An alternative hypothesis is the negation of null hypothesis, for example, "this person is not healthy", "this accused is guilty" or "this product is broken". The result of the test may be negative, relative to null hypothesis (not healthy, guilty, broken) or positive (healthy, not guilty, not broken). If the result of the test corresponds with reality, then a correct decision has been made. However, if the result of the test does not correspond with reality, then an error has occurred. Due to the statistical nature of a test, the result is never, except in very rare cases, free of error. Two types of error are distinguished: type I error and type II error.
Read more about this topic: Type I And Type II Errors
Famous quotes containing the words test and/or theory:
“I am willing, for a money consideration, to test this physical strength, this nervous force, and muscular power with which I’ve been gifted, to show that they will bear a certain strain. If I break down, if my brain gives way under want of sleep, my heart ceases to respond to the calls made on my circulatory system, or the surcharged veins of my extremities burst—if, in short, I fall helpless, or it may be, dead on the track, then I lose my money.”
—Ada Anderson (1860–?)
“Everything to which we concede existence is a posit from the standpoint of a description of the theory-building process, and simultaneously real from the standpoint of the theory that is being built. Nor let us look down on the standpoint of the theory as make-believe; for we can never do better than occupy the standpoint of some theory or other, the best we can muster at the time.”
—Willard Van Orman Quine (b. 1908)