External Vs Internal Validity: What Is the Difference Between the Two? (With Examples)

External Vs Internal Validity: What Is the Difference Between the Two? (With Examples)
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The term “validity” refers to the approximate truth of inferences, propositions, or conclusions in the research field. The two available types of validity are critical parameters that researchers use to evaluate a procedure’s or research study’s validity. 

These two are pretty different. The latter refers to the extent to which researchers can claim that the result was caused only by the variable they’re studying. On the other hand, external validity is the extent to which a study’s results must be generalized to a global audience. But what is the difference between internal validity and external validity quizlet?

This detailed guide will take you through internal validity vs external validity to give you a deeper understanding of both.

What Is Internal Validity

This phrase refers to the degree of confidence that a causal relationship exists between the treatment and outcome differences. In other words, it seeks to answer these questions:

  • How’s the researcher’s performance during the study?
  • What’s the likelihood that your treatment resulted in the differences in observed results
  • How correct are the conclusions?
  • Did other causes lead to outcome changes?

To establish internal validity, you must assess the data collection process, the experimental design, data validity and reliability, and other elements like the experiment’s duration and setting. You may have to understand natural processes and events occurring outside the study.
Generally, a high internal validity degree provides strong casualty evidence.

Factors That Improve Internal Validity

Improving your study’s internal validity requires you to consider several critical research design aspects that increase your likelihood of rejecting alternative hypotheses. The following factors are crucial:

  • Binding – Participants and researchers who don’t know the intervention they’re receiving to avoid biasing their behaviors and perceptions and thus the study outcome
  • Experimental manipulation – Where you manipulate an independent variable instead of observing it without any interviews
  • Random selection – Choosing participants randomly to represent a population you wish to study
  • Randomization – Where you randomly assign participants to control and treatment groups and avoid any systematic bias
  • Study protocol – Paying attention to specific procedures for administering treatment to avoid doing things differently with distinct groups

Threats to Internal Validity

Consider the following potential threats to internal validity when planning your study:

  • Attrition – When participants leave a study, your results will be based on a biased sample comprising the people who chose to stay.
  • Statistical regression – Participants at a measure’s extreme ends may naturally fall in a specific direction due to time passage and not an effect of the intervention.
  • Testing – Repeated tests with the same approach changes outcomes. If someone is tested three times, they’ll likely do better and answer differently after getting used to the process.
  • Maturation – This is the impact of time as a study variable. Comprehensive studies have the potential of participants’ natural changes, and it can be challenging to determine whether the effect of time caused the effects
  • Experimenter bias – An experiment can behave differently with varying study groups, significantly impacting the study.

Confounding, diffusion, historical events, and instrumentation are other potential threats worth considering. 

What Is an Example of External Validity?

A classic example is whether typical psychology or economics lab experiments produce results that you can generalize to the broader public. In the political development economy, you may consider whether and how a community-focused program in Canada may apply to Central America or Eastern Africa.

What is External Validity?

External validity in an experimental design refers to how you generalize a research study’s conclusion to the world at large. A research’s primary goal is to make inferences about how things work based on study results. 

For instance, you can generalize a whole population’s study results based on a sample population. But you can’t make such inferences if you don’t have external validity, and the study won’t reveal accurate facts about the environment beyond the study.
Researchers can increase their studies’ external validity by leveraging proximal similarity and sampling models.

Factors That Improve External Validity

Here’s how to boost your study’s external validity:
• Psychological realism – The participants must experience your study’s events as accurate by learning about the study’s aim through a cover story to avoid them behaving differently than in real life.
• Calibration or reprocessing – Statistical methods like reweighing groups with uneven characteristics can help you adjust to issues related to external validity.
• Replication – Check if you’ll find the same results if you research again in different settings or with different samples. After multiple studies, you’ll determine the reliability of an independent variable using a meta-analysis.
• Field experiments – Research in a natural setting outside the lab.
• Use exclusion and inclusion criteria – This ensures a clearly defined study population.

Leading Threats to External Validity

This validity is threatened when your research doesn’t consider how variables interact in the real world. Common threats include:
• Pre- or post-test effects – When these relate in any way to the study’s notable effect, the cause-and-effect relationship will disappear without the additional tests.
• Sample features – The findings will have limited generalizability if a sample feature is responsible for the effect.
• Selection bias – These are the differences between groups in research relating to the independent variable.
• Situational factors – Location, time of day, noise, used measures, and researcher attributes may affect findings generalization.

What Is an Internal Validity Example?

You’ve arranged volunteers for a test on the health impacts of an apple, and you’ve selected, grouped, and scheduled the subjects for participation. You’ll test the health state of the subjects then administer an apple each for the entire duration. 

After analyzing test results, you realize that the test group was better than the control group, but this isn’t sufficient proof. Internal validity will be critical in proving each finding’s trustworthiness. 

Internal vs. External Validity

So what is the difference between internal validity and external validity? 
Generally, the two are like the two sides of a coin. While your study may have good internal validity, it could still be irrelevant to the real world. Then again, you could conduct more relevant field research, but this may not have trustworthy findings in terms of understanding the variables that led to the evident outcomes.

And which is more important between internal validity and external validity? Your design must have two forms of validity. But the internal one is the most critical requirement, and you must include it in your experiment before you draw any inferences regarding the effects of treatment. You should control extraneous validity to establish the internal one.


Both must be included in a study, and their implications are based on whether the study results have meaning. They’re not concepts, so you should decide your study’s degree of your study performance for both validities. Moreover, you must report each of them in your research article to allow others to evaluate your study and decide the validity or usefulness of the results.


Internal validity covers your study's structure and variables, while external validity refers to the universal nature of the study results. Your experiment's design will determine its validity.
Other notable differences between internal and external validity include:
• Internal validity is concerned with control and measures the experiment's accuracy, while external validity determines whether the test's causal relationship can be generalized.
• The former identifies the strength of research methods, while the latter focuses on whether the outcomes can apply to the real world.
• Internal validity describes the conclusion's warranted level, while external validity defines the degree to which the research generalizes the result to other contexts.
• The former eliminates or addresses alternative results explanations while the latter generalizes the outcome.

Final Thoughts

An experiment design should have both validity categories. Internal validity is the most crucial requirement, and you must present it in an experiment before drawing inferences concerning treatment effects. You must control extraneous validity to establish internal validity. External validity, on the other hand, forms a great experiment design's cornerstone and is a bit complex to achieve. If you still want to better understand the differences and similarities between external and internal validity don't hesitate to contact our writers.

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