Beyond the Bell Curve: Why Outliers Matter in Research and Social Justice
The Illusion of Objectivity in Statistics
Throughout my academic career I was told that quantitative methods were the gold standard of research methods. And yes, I completely agree that when applied responsibly, quantitative analyses are reasonably objective and not inherently deceitful. However, the way data is collected, interpreted, and used can shape societal narratives that either include or exclude marginalized groups.
Numbers tell a story, but they don’t always tell the whole story.
Since my first college statistics course, I have wrestled with the concept of the bell curve, a visualization of “normal distribution.” It wasn’t the math that troubled me—it was the underlying message that “normal” equated to the majority. Outliers—those who exist at the fringes of the distribution—are often controlled for or dismissed to make data cleaner and conclusions more generalizable. But what happens when those outliers represent real people whose experiences diverge from the mainstream? When we remove the outliers, we don’t make them cease to exist—we erase them from the conversation.
The Social Justice Argument for Outliers
In the wake of Inauguration Day, a presidential decree declared,
It is the policy of the United States to recognize two sexes, male and female. These sexes are not changeable and are grounded in fundamental and incontrovertible reality. Under my direction, the Executive Branch will enforce all sex-protective laws to promote this reality” (source: whitehouse.gov).
While this policy does not erase the existence of nonbinary and gender-diverse individuals, it does attempt to erase their value in society (see examples).
A policy recognizing only two genders does more than define legal classifications—it cuts off the tails of the bell curve. It removes the opportunity for certain individuals to even be counted as outliers. Social privilege argues that outliers don’t matter because there isn’t enough data to support their existence. Social justice research argues the opposite: outliers matter precisely because they are underrepresented and underserved.
And then people ask, why are marginalized individuals— those who are LGBTQ+, Black, Indigenous, People of Color, immigrants, refugees, women, people with disabilities, and others— so loud? Why must they demand visibility? This is why. When policies and data collection practices systematically erase identities, advocacy is not just necessary, it is survival.
The Power of Qualitative Research in Advocacy
Marginalized communities have historically been ignored in quantitative research because the samples are usually too small to be statistically significant. But their realities are significant. Their experiences demand recognition, and their voices deserve amplification.
As a qualitative researcher, I have focused my career on engaging the rich experiences of those on the margins—voices with valuable insights. While qualitative researchers use systematic methods to seek patterns within data, they also value nuance, complexity, and lived experience. By elevating the narratives of those at the fringes, we move toward a society where visibility is not a privilege but a right. Here are a few strategies that ensure marginalized perspectives remain central in qualitative research:
1. Broad, Not Vague: Crafting Inclusive Research Questions
Research questions should allow for variation while staying focused on the topic of interest. The complexity of human experience, particularly within marginalized groups, demands research methods that accommodate diverse narratives. By designing broad yet specific inquiries, we create space for voices that might otherwise be overlooked.
2. Counter-Narratives: Centering the Voices of the Marginalized
Counter-narratives provide a platform for those whose experiences challenge dominant societal assumptions. When institutions erase identities, counter-narratives restore visibility. These stories do not just complement quantitative findings; they offer necessary correctives to incomplete data.
3. Emphasizing Lived Experience Over Generalizability
Traditional research often prioritizes findings that can be generalized to large populations. However, to generate social change, the goal is not always generalizability—it’s understanding. Centering lived experience helps us grasp the realities of those who exist at the fringes of the bell curve. Their experiences inform policies, healthcare, education, and social services in ways that numbers alone cannot.
Conclusion: Making the Invisible Visible
Eliminating outliers does not eliminate people.
Ignoring experiences does not make them any less real.
If research is to serve justice, it must recognize the full spectrum of human experience—not just the statistical majority.
Because in the end, what we choose to measure—and whom we choose to listen to—defines whose lives we consider valuable.