Search results
Results From The WOW.Com Content Network
[B] A theory or hypothesis is falsifiable if it can be logically contradicted by an empirical test. Popper emphasized the asymmetry created by the relation of a universal law with basic observation statements [ C ] and contrasted falsifiability to the intuitively similar concept of verifiability that was then current in logical positivism .
Falsifiability or defeasibility, which means that counterexamples to the hypothesis are logically possible. The practical feasibility of observing a reproducible series of such counterexamples if they do exist. In short, a hypothesis is testable if there is a possibility of deciding whether it is true or false based on experimentation by anyone.
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted.
Statistical significance is formalized in terms of probability, with its p-value measure being reported in the scientific literature as a screening mechanism. Ioannidis posited assumptions about the way people perform and report these tests; then he constructed a statistical model which indicates that most published findings are likely false ...
The hypothetico-deductive model or method is a proposed description of the scientific method.According to it, scientific inquiry proceeds by formulating a hypothesis in a form that can be falsifiable, using a test on observable data where the outcome is not yet known.
In statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm rate [1]) is the probability of falsely rejecting the null hypothesis for a particular test.
The false positive rate is equal to the significance level. The specificity of the test is equal to 1 minus the false positive rate. In statistical hypothesis testing, this fraction is given the Greek letter α, and 1 − α is defined as the specificity of the test. Increasing the specificity of the test lowers the probability of type I errors ...
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.