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A theory or explanation is hard to vary if all details play a functional role, i.e., cannot be varied or removed without changing the predictions of the theory. Easy to vary (i.e., bad) explanations, in contrast, can be varied to be reconciled with new observations because they are barely connected to the details of the phenomenon of question.
The New York Times of November 10, 1919, reported on Einstein's confirmed prediction of gravitation on space, called the gravitational lens effect.. The concept of predictive power, the power of a scientific theory to generate testable predictions, differs from explanatory power and descriptive power (where phenomena that are already known are retrospectively explained or described by a given ...
Best linear unbiased predictions" (BLUPs) of random effects are similar to best linear unbiased estimates (BLUEs) (see Gauss–Markov theorem) of fixed effects. The distinction arises because it is conventional to talk about estimating fixed effects but about predicting random effects, but the two terms are otherwise equivalent.
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. Calibration can mean a reverse process to regression, where instead of a future dependent variable being predicted from known explanatory variables, a known observation of the dependent variables is used to predict a corresponding explanatory variable; [1]
Michael Fish - A few hours before the Great Storm of 1987 broke, on 15 October 1987, he said during a forecast: "Earlier on today, apparently, a woman rang the BBC and said she heard there was a hurricane on the way.
Statistical testing uses data from samples to assess, or make inferences about, a statistical population.For example, we may measure the yields of samples of two varieties of a crop, and use a two sample test to assess whether the mean values of this yield differs between varieties.
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Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...