Those developing models have found this situation disquieting (Bredehoeft and Konikow 1993) because, even though science thrives on the competition of ideas, when two different models yield clearly contradictory results, as a matter of logic, they cannot both be true. 1986), or at least to a plurality of legitimate perspectives on the given issue, with each such perspective buttressed by a model proclaimed to be valid. Great uncertainty can lead paradoxically to a situation of “contradictory certainties” (Thompson et al.
#A RANGE CHECK IS A DATA VALIDATION CHECK THAT ____ LICENSE#
When there is great uncertainty surrounding the science base of an issue, groups of stakeholders within society can take this issue as a license to assert utter confidence in their respective versions of the science, each of which contradicts those of the other groups. The public knows well that supposedly authoritative scientists can have diametrically opposed views on the benefits of proposed measures to protect the environment. Because awareness of environmental regulatory models has become so widespread in a more scientifically aware audience of stakeholders and the public, words used within the scientific enterprise can have meanings that are misleading in contexts outside the confines of the laboratory world. The terms “validation” and “assurance” prejudice expectations of the outcome of the procedure toward only the positive-the model is valid or its quality is assured-whereas evaluation is neutral in what might be expected of the outcome. The difficulty in finding a label for the process of judging whether a model is adequate and reliable for its task is described as follows. In this restricted sense, “validation” is still a part of the common vocabulary of model builders. Two decades ago, model “validation” (as it was referred to then) was defined as the assessment of a model’s predictive performance against a second set of (independent) field data given model parameter (coefficient) values identified or calibrated from a first set of data. Evaluation emerged from this debate as the most appropriate descriptor and is characteristic of a life-cycle process. Some of these terms imply, innately or by their de facto use,Ī one-time approval step. Although it might seem strange for such a label to be important, earlier terms used for describing the process of judging model performance have provoked rather vigorous debate, during which the word “validation” was first to be replaced by “history matching” (Konikow and Bredehoeft 1992) and later by the term “quality assurance” (Beck et al. 1994) that complex computational models can never be truly validated, only “invalidated.” The contemporary phrase for what one seeks to achieve in resolving model performance with observation is “evaluation” (Oreskes 1998). Although “model validation” became a common term for judging model performance, it has been argued persuasively (e.g., Oreskes et al. This issue has long been a matter of great interest, marked by many papers over the past several decades, but especially and distinctively by Caswell (1976) who observed that models are objects designed to fulfill clearly expressed tasks, just as hammers, screwdrivers, and other tools have been designed to serve identified or stated purposes. the month of the year must be between 1 and 12.How does one judge whether a model or a set of models and their results are adequate for supporting regulatory decision making? The essence of the problem is whether the behavior of a model matches the behavior of the (real) system sufficiently for the regulatory context. It checks the value of data to see if it is within a certain range e.g.
For example, if a form field only accepts values between 1 and 99, a range validation rejects the number 100. One may also ask, when would a range check be used? Range validations are used to ensure that data received via input matches the expected range limitations imposed by the application. The computer can be programmed only to accept numbers between 11 and 16. Validation is an automatic computer check to ensure that the data entered is sensible and reasonable. Remember that this does not necessarily mean that the data entered will be correct.īesides, what is a data type check validation? This is done to ensure that only numbers within a certain domain can be entered into a field. Range Check – Range check is a validation check which can be applied to numeric fields.