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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.
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.
Validity has two distinct fields of application in psychology. The first is test validity (or Construct validity ), the degree to which a test measures what it was designed to measure. The second is experimental validity (or External validity ), the degree to which a study supports the intended conclusion drawn from the results.
The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
There are five key principles relating to internal validity (study design) and external validity (generalizability) which rigorous impact evaluations should address: confounding factors, selection bias, spillover effects, contamination, and impact heterogeneity. [5]
A distinction of sampling bias (albeit not a universally accepted one) is that it undermines the external validity of a test (the ability of its results to be generalized to the rest of the population), while selection bias mainly addresses internal validity for differences or similarities found in the sample at hand. In this sense, errors ...
Field experiments offer researchers a way to test theories and answer questions with higher external validity because they simulate real-world occurrences. [6] Some researchers argue that field experiments are a better guard against potential bias and biased estimators. As well, field experiments can act as benchmarks for comparing ...
Reliability does not imply validity. That is, a reliable measure that is measuring something consistently is not necessarily measuring what you want to be measured. For example, while there are many reliable tests of specific abilities, not all of them would be valid for predicting, say, job performance.