Viral post, cherry-picked data, and how to tell the full story with honest, data-backed insights

I recently came across a viral LinkedIn post from Daniel Priestley about wealth inequality that claimed of technological disruption is the primary driver of inequality and individual adaptation is the only solution. The post was compelling, used data…but it oversimplified a much more complex issue.

The post falls for two common pitfalls that information designers should recognize:

• Selective evidence (also known as cherry picking): Priestley uses the 50-year stretch of zero wage growth, known as the "Engels Pause" (1790–1840), to argue that new technology caused inequality. But this ignores other historical periods, like the post-WWII era, when technology advanced rapidly while inequality actually decreased.

• False causality (or correlation doesn't imply causation): He primarily blamed inequality on technology adoption. Totally overlooking the broader factors at play, like policy choices, education, labor rights, and many more that contribute to economic disparities.

Let me be clear, I don't think Daniel Priestley was deliberately manipulating data. Rather, he's likely expressing confirmation bias: our very human tendency to notice information that confirms existing beliefs while filtering out contradictory evidence. We all do it.

The thing is, we see the world shapes the stories we tell. In this case, his perspective leans heavily on an individualist mindset, where personal effort is often seen as the solution to any problem (or the cause of one). Spoiler alert: Life success is not just about the hustle.

So how can we present a more nuanced, data-driven narrative that resonates with people who share his individualistic perspective?

The trick is to remember that people are more likely to engage with information when it is framed in a way that aligns with how they see the world, with their personal and group values.

Applying this to data visualization means:

1. Choosing data that shows systems not individuals

Avoid: showing data that highlights individual experiences like the Percentage of Black vs. white households with broadband access (individual framing).

Instead: choose a dataset that demonstrate systems and infrastructure impact. One example could be showing how specific policies (municipal broadband, technology subsidies, digital literacy programs) connect to measurable increases in tech access across all demographic.

This shifts the focus from personal failure to systemic solutions.

2. Showing "Same input, Different results"

Avoid: presenting a single snapshot of data (like inequality in a specific country during one period)

Instead: showing side-by-side comparisons where the key factor is the same, but outcomes different. For example, we could show Nordic countries vs. US, with similar technology adoption that lead to different inequality outcomes based on policy, law, culture and social eenvironments

This shows that there's more to the story than just one factor. It naturally raises the question "why the difference?", inviting curiosity rather than defensiveness.

3. Using language that bridges value systems

For an audience that values personal agency, frame the conversation in terms they care about:

We must ensure that technology isn’t just for the privileged few but accessible to anyone ready to leverage it for their future.

The goal is to acknowledge their core belief in personal empowerment, while subtly expanding the conversation to consider the role of systems and policies that influence outcomes.

See you next time,

Gabby from rainy California

NB: I owe you a newsletter about inspiration for dataviz, but the classic “just work harder and you’ll succeed” post really got under my skin, so I had to write about it to feel better. I’ll get back to the regular content soon. Promise!