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External validity is the validity of applying the conclusions of a scientific study outside the context of that study. [1] In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times.
Data validation is intended to provide certain well-defined guarantees for fitness and consistency of data in an application or automated system. Data validation rules can be defined and designed using various methodologies, and be deployed in various contexts. [1]
Data cleaning differs from data validation in that validation almost invariably means data is rejected from the system at entry and is performed at the time of entry, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities.
Such final external validation requires the use of an acceptance test which is a dynamic test. However, it is also possible to perform internal static tests to find out if the software meets the requirements specification but that falls into the scope of static verification because the software is not running.
It is often an internal process. Contrast with validation." Similarly, for a Medical device, the FDA defines Validation and Verification as procedures that ensures that the device fulfil their intended purpose. Validation: Ensuring that the device meets the needs and requirements of its intended users and the intended use environment.
Psychotherapist Lisa Brateman has criticized thirst traps as being an unhealthy method of receiving external validation. [10] [29] This desire for external validation can be addictive. [29] Thirst traps can cause pressure to maintain a good physical appearance, and therefore cause self-esteem issues.
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
In statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model.