Data PitfallsThe Unheard Voice
What it is
Whose voices, sources, and contributions are represented in the data work — and whose are missing — goes unexamined, so perspectives that have historically been undervalued or ignored are left out, and their contributions are under-credited or misattributed. The result is a biased baseline: the data, sources, examples, and narrative over-represent dominant groups and quietly erase others.
How to spot it
In prose, sources, and data, flag a body of work whose cited sources, quoted experts, examples, or training data draw from a narrow, non-representative set of voices (e.g. all-male quote lists, single-demographic expert panels), contributions attributed to a later or better-known figure while the originator is omitted, and datasets or samples that exclude or undercount historically marginalized groups. Be suspicious when no one has asked whose perspective is missing or who deserves credit.
Pitfall
A book's nine chapter epigraphs are all quotes from men, with not one woman — and its discussion of the box plot credits only Tukey, omitting Mary Eleanor Spear, whose 1952 "range-bar" was its precursor.
Done well
The author audits representation before publishing — deliberately sourcing quotes and examples from a diverse range of voices and crediting originators like Spear alongside Tukey — so the work reflects contributions in proportion to their value, not the demographic of the contributor.
How to fix it
Deliberately check whose voices and contributions are represented and whose are missing before finalizing data work. Seek out and amplify historically undervalued or ignored voices in your sources, examples, and data; credit originators accurately; and treat representation as a question to ask explicitly, not assume.
Avoiding Data Pitfalls (Wiley, 2020), Pitfall 8A: The Unheard Voice