3.2 Limitations: External validity

External validity refers to the ability to generalise the results to other groups in the population apart from the sample studied (see section: External and internal validity). For a study to be externally valid, it must first be internally valid.

Importantly, external validity refers to how well the sample is likely to represent the target population as given in the RQ.

For example, suppose the RQ is 'Among Queenslanders, what proportion own a smart speaker?'.

The study is externally valid if the sample is representative of Queenslanders, and hence the results from the sample are likely to apply to Queenslanders as a whole.

The results do not have to apply to people in the rest of Australia. The intended population, as given in the RQ, is Queenslanders.

External validity refers to the applicability or the generalisability of the results to the target (or intended) population (Example), which depends on how the sample was obtained: results from random samples are likely to generalise to the population and be externally valid when appropriately analysed. (The analyses in this subject assume all samples are simple random samples.) Furthermore, results from approximately representative samples may generalise to the population and be externally valid if those in the study are not obviously different than those not in the study.

Example 3.2 (External validity) A New Zealand study (Gammon et al. 2012) identified (for well-documented reasons) a particular group to study: 'women of South Asian origin living in New Zealand' (p. 21).

The women in the sample came from a study

... which investigated the health and lifestyle of women of South Asian origin living in New Zealand. Subjects [...] were recruited using a convenience sample method throughout Auckland, which is New Zealand's largest city, and the city in which most South Asian immigrants settle...

--- (Gammon et al. (2012), p. 21)

The results may not generalise to the intended population of 'women of South Asian origin living in New Zealand' because all the women in the sample came from only one city in New Zealand (Auckland), and the sample was not a random sample.

The results will not generalise to all New Zealand women, but this is not a limitation: the target population was only 'women of South Asian origin living in New Zealand'. The researchers did not intend the results to apply to all New Zealnd women.

Example 3.3 (Using biochar) A study of using biochar (Farrar et al. 2018) to grow ginger only used one farm at Mt Mellum, Australia.

While the results may only apply to growing ginger at Mt Mellum, the encouraging results suggest that a wider, more general, study of the impact of using biochar to grow ginger would be worthwhile.

In addition, ginger is usually grown is similar types of climates and soils, so the results may apply to other ginger farms also.


Farrar, Michael B., Helen M. Wallace, Cheng-Yuan Xu, Thi Thu Nhan Nguyen, Ehsan Tavakkoli, Stephen Joseph, and Shahla Hosseini Bai. 2018. “Short-Term Effects of Organo-Mineral Enriched Biochar Fertiliser on Ginger Yield and Nutrient Cycling.” Journal of Soils and Sediments, 1–15.
Gammon, Cheryl S., Pamela R. von Hurst, Joan Coad, Rozanne Kruger, and Welma Stonehouse. 2012. “Vegetarianism, Vitamin B12, and Insulin Resistance in a Group of Predominately Overweight/Obese South Africian Women.” Nutrition 28: 20–24.