Threats to Validity

Students are introduced to the concept of validity, and a number of possible threats that might make an analysis invalid.

Let’s say a survey says that people prefer cats to dogs…​

As good Data Scientists, the staff at the animal shelter are constantly gathering data about their animals, their volunteers, and the people who come to visit. But just because they have data doesn’t mean the conclusions they draw from it are correct! For example: suppose they surveyed 1,000 cat-owners and found that 95% of them thought cats were the best pet. Could they really claim that people generally prefer cats to dogs?

Have students share back what they think. The issue here is that cat-owners are not a representative sample of the population, so the claim is invalid.

There’s more to data analysis than simply collecting data and crunching numbers. In the example of the cat-owning survey, the claim that “people prefer cats to dogs” is invalid because the data itself wasn’t representative of the whole population (of course cat-owners are partial to cats!). This is just one example of what are called Threats to Validity.

There are several major threats to validity you should be on guard against:

(1) Selection bias - Data was gathered from a biased sample of the population. This is the problem with surveying cat owners to find out which animal is most loved!

(2) Bias in the study design - Data was gathered using a “loaded” question like “Since annual vet care comes to about $300 for dogs and only about half of that for cats, would you say that owning a cat is less of a burden than owning a dog?” This could easily lead to a misrepresentation of people’s true opinions.

(3) Poor choice of summary data - Even if the selection is unbiased, sometimes outliers are so extreme that they make the mean completely useless at best - and misleading at worst.

(4) Confounding variables - A study might find that cat owners are more likely to use public transportation than dog owners. But it’s not that owning a cat means you drive less: people who live in big cities are more likely to use public transportation, and also more likely to own cats. More examples of confounding variables can be found in the correlations lesson: Correlation Does Not Imply Causation!.

This is just a small list of different threats to validity. There are plenty more!

Give students time to discuss and share back.

Life is messy, and there are always threats to validity. Data Science is about doing the best you can to minimize those threats, and to be up front about what they are whenever you publish a finding. When you do your own analysis, make sure you include a discussion of the threats to validity!

Students are asked to consider the ways in which statistics are misused in popular culture, and become critical consumers of some statistical claims. Finally, they are given the opportunity to misuse their own statistics, to better understand how someone might distort data for their own ends.

Students have already seen a number of ways that statistics can be misused:

There are other ways to mislead the audience as well:

With all the news being shared through newspapers, television, radio, and social media, it’s important to be critical consumers of information!

Have students share back their "lies". Was anyone able to stump the other group?