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No One Is an Asterisk
“We are a small population of people because of genocide. No other reason. If you eliminate us in the data, we don’t exist. We don’t exist for the allocation of resources”.
– Abigail Echo-Hawk, Pawnee, Director of the Urban Indian Health Institute and Chief Research Officer of the Seattle Indian Health Board
There is a world of difference between saying “we’re less certain” and “they don’t count.” If you have a small sample size for a certain group of people, what you can say about them might be less “certain” or “reliable” than you want, but that doesn’t mean we should discount their data with terms like “not statistically significant.” It’s not mathematically correct, and it’s dehumanizing and harmful.
Everyone wants data results with clear meanings that can be relied upon. How much weight we can give to any answer does depend on sample size, but it also depends on sample quality, sample similarity, and what we’re measuring. Sample size isn’t everything when it comes to “certainty.”
A long and rigid tradition in statistical science has been to suppress the results from small sample sizes or neutralize them by calling them “not statistically significant.” The fear of having our work discounted because of high levels of uncertainty often leads us to transfer that burden onto the minority groups in our samples — better that they…