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Type I and type II errors. In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. For example, an innocent person may be convicted. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false.
The formulated hypothesis is then evaluated, where the hypothesis is proven to be either "true" or "false" through a verifiability- or falsifiability-oriented experiment. [11] [12] Any useful hypothesis will enable predictions by reasoning (including deductive reasoning).
Null hypothesis. In scientific research, the null hypothesis (often denoted H0) [1] is the claim that the effect being studied does not exist. [note 1] The null hypothesis can also be described as the hypothesis in which no relationship exists between two sets of data or variables being analyzed. If the null hypothesis is true, any ...
A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p -value computed from the test statistic. Roughly 100 specialized statistical tests have been defined. [ 1 ][ 2 ]
Falsifiability (or refutability) is a deductive standard of evaluation of scientific theories and hypotheses, introduced by the philosopher of science Karl Popper in his book The Logic of Scientific Discovery (1934). [B] A theory or hypothesis is falsifiable if it can be logically contradicted by an empirical test.
The formulated hypothesis is assumed to be 'true', and from that 'true' statement implications are inferred. If the following tests show the implications to be false, it follows that the hypothesis was false also. If test show the implications to be true, new insights will be gained.
Riemann hypothesis. This plot of Riemann's zeta (ζ) function (here with argument z) shows trivial zeros where ζ (z) = 0, a pole where ζ (z) = , the critical line of nontrivial zeros with Re (z) = 1/2 and slopes of absolute values. In mathematics, the Riemann hypothesis is the conjecture that the Riemann zeta function has its zeros only at ...
In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses ) that are false (incorrect rejections of ...