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External Validity Definition & Examples

Statistics Definitions > External Validity

External Validity Definition

External validity helps to answer the question: can the research be applied to the “real world”? If your research is applicable to other experiments, settings, people, and times, then external validity is high. If the research cannot be replicated in other situations, external validity is low. It’s important to know that your research is effective (internal validity) and that it is effective in other situations.

Historically, researchers have focused on internal validity. The scientific rigor of randomized, controlled experiments was often thought to be more important than the generalizability of results. More recently, researchers have been aiming for research that is more generalizable outside the lab. However, this isn’t as easy as it seems. External validity is one of the most difficult types of validity to achieve. One reason for this is that steps to make external validity high often result in a lowering of internal validity. Another reason is the multitudes of hidden and confounding variables that can affect your experimental outcome.

Population Validity and Ecological Validity

Population validity and ecological validity are types of external validity.

  • Population validity answers the question: how well can the research on a sample be generalized to the population as a whole?
  • Ecological validity answers the question: are your study results generalizable across different settings?

Threats to External Validity

external validity

Hidden variables and factors in an experiment can taint your results, making them ungeneralizable.

Threats to external validity compromise your confidence in stating that your study results are applicable to other situations. They are explanations of how you might be wrong in making generalizations. For example, your conclusion might be incorrect, the changes in the dependent variable may not be due to changes in the independent variable, and variation in the dependent variable might be due to other causes. For example, extraneous variables may be competing with the independent variable to explain the study outcome.

Some specific examples of threats to external validity:

  • Is your sample selected randomly? If not, it may be open to selection bias.
  • Have you included a pretest? In some experiments, pretests may influence the outcome. A pretest might clue the subjects in about the ways they are expected to answer or behave.
  • Are your participants taking multiple versions of the same test? If so, the practice effect might influence your results. For example the Wechsler Intelligence Scale for Children is highly influenced by the practice effect.
  • Is your sample composed of a homogeneous population, like all low achievers or all high achievers? If so, your results probably won’t be generalizable to the “average” person.
  • Are the results of your study tainted by the Hawthorne effect? Your study participants may be behaving differently because they know they are in an experimental study.

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Statistical concepts explained visually - Includes many concepts such as sample size, hypothesis tests, or logistic regression, explained by Stephanie Glen, founder of StatisticsHowTo.

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