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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.
In other words, the relevance of external and internal validity to a research study depends on the goals of the study. Furthermore, conflating research goals with validity concerns can lead to the mutual-internal-validity problem, where theories are able to explain only phenomena in artificial laboratory settings but not the real world. [13] [14]
A problem statement is a description of an issue to be addressed, or a condition to be improved upon. It identifies the gap between the current problem and goal. The first condition of solving a problem is understanding the problem, which can be done by way of a problem statement. [1]
Ecological validity, the ability to generalize study findings to the real world, is a subcategory of external validity. [ 6 ] Another example highlighting the differences between these terms is from an experiment that studied pointing [ 7 ] —a trait originally attributed uniquely to humans—in captive chimpanzees.
The interesting result is that consideration of a real population and a real sample produced an imaginary bag. The philosopher was considering logic rather than probability. To be a real statistical hypothesis test, this example requires the formalities of a probability calculation and a comparison of that probability to a standard.
Problem discovery is an unconscious process which depends upon knowledge whereby an idea enters one's conscious awareness, problem formulation is the discovery of a goal; problem construction involves modifying a known problem or goal to another one; problem identification represents a problem that exists in reality but needs to be discovered ...
Statistical inference makes propositions about a population, using data drawn from the population with some form of sampling.Given a hypothesis about a population, for which we wish to draw inferences, statistical inference consists of (first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model.
[28] [29] [30] Research projects that use working hypotheses use a deductive reasoning or logic of inquiry. [3] In other words, the problem and preliminary theory are developed ahead of time and tested using evidence. Working hypotheses (statements of expectation) are flexible and incorporate relational or non-relational statements.