Data refers to a collection of facts or statistics that are all related to each other. Every company collects many different types of data and uses it to analyze trends, project future values, and make informed decisions. Researchers must back up their claims with tests using data. The result of a researcher’s work is as good as their data. With limited data, it is hard to make informed decisions about the subjects left out. While data is so important, many groups of people have been left out of testing. One the groups that have been historically left out, and still are today, are women.

Developing a new product or conducting medical trials requires testing on a variety of different people. However, even if a sample is “randomized,” women are often excluded from the picture. I was introduced to this topic in Caroline Criado-Perez’s book “Invisible Women: Data Bias in a World Designed by Men.” This book highlights how many products and services are designed without thinking about women. An example of this is how cars are built and tested. Car crash testing dummies are based on the “average” person, which in this case is a man. These tests completely discount the differences between male and female anatomy in factors like height and weight differences. Another example of this is in the medical field in which many researchers only test medications on male mice because female mice undergo “too many changes.” The effects of non-inclusive testing has even had negative effects on the livelihood of women. For example, research states: “Between 1997 and 2000, eight of the ten drugs withdrawn from the market posed a greater health risk for women either due to unanticipated gender-prescribing trends or sex-specific adverse drug reactions… (4).”

Inclusive testing in product development is important so that solutions are similarly inclusive. Forgetting about women when testing and analyzing data puts half of the world’s population at a higher risk. Including women in testing, research, and development is a necessary step to reduce data bias and to improve the quality of lives for many more people.

~ Written by Laasya Aki, Edited by Rita Dwivedi

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