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Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.
George Box. The phrase "all models are wrong" was first attributed to George Box in a 1976 paper published in the Journal of the American Statistical Association.In the paper, Box uses the phrase to refer to the limitations of models, arguing that while no model is ever completely accurate, simpler models can still provide valuable insights if applied judiciously. [1]
Economic models can be such powerful tools in understanding some economic relationships that it is easy to ignore their limitations. One tangible example where the limits of economic models allegedly collided with reality, but were nevertheless accepted as "evidence" in public policy debates, involved models to simulate the effects of NAFTA ...
Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and ...
When parametric statistics are used beyond their specifications, Econometricians argue that the insight will exceed the inaccuracy while Austrians argue that the inaccuracy will exceed the insight. A historical example of this debate is the Friesh–Leontief "Pitfalls" debate, with Friesh holding the Austrian position and Leontief holding the ...
Given that the validity of any conclusion drawn from a statistical inference depends on the validity of the assumptions made, it is clearly important that those assumptions should be reviewed at some stage. Some instances—for example where data are lacking—may require that researchers judge whether an assumption is reasonable. Researchers ...
For one perspective on research in robust statistics up to 2000, see Portnoy & He (2000). Some experts prefer the term resistant statistics for distributional robustness, and reserve 'robustness' for non-distributional robustness, e.g., robustness to violation of assumptions about the probability model or estimator, but this is a minority usage ...
The word Bayesian appeared around 1950, and by the 1960s it became the term preferred by those dissatisfied with the limitations of frequentist statistics. [ 62 ] [ 65 ] In the 20th century, the ideas of Laplace were further developed in two different directions, giving rise to objective and subjective currents in Bayesian practice.