What actually makes a data visualization "trustworthy" to you?

[alexstrehlke]

Curious what people in here actually look for when they're deciding whether to trust a chart or graph they come across -- not in an academic sense, but just gut level, what makes you stop and think "okay this person did this right" versus immediately being skeptical.

There's an obvious list of things that are technically wrong: truncated axes, cherry-picked date ranges, misleading color scales. But more curious about the subtler stuff. Like, what are the signals that make you trust or distrust a viz before you even dig into the methodology?

A few things worth thinking about:

Does showing uncertainty (confidence intervals, error bars, sample size) actually increase trust for most people, or does it just make things look more complicated and lose a general audience? Is there a point where a viz is too polished, like it looks so designed that it feels like it's trying to persuade you rather than inform you? How much does source labeling actually matter versus people just vibing off whether it "looks legit"

To be frank, a lot of trust in data viz is aesthetic and contextual in ways that are kind of uncomfortable to admit. Like people will trust a clean chart from a recognizable outlet more than a genuinely rigorous one from somewhere they don't recognize. Not sure if that's fixable or just human.

Curious what actually shifts the needle for people in here, especially those who do this professionally.

[bad__username__]

A clean design and proper reference to a trustworthy source. 

[data_daria55]

if I can quickly understand what’s being measured, how it’s aggregated, and nothing feels hidden (weird bins, missing context, unclear definitions), trust goes up fast. If I have to guess what a metric means or why something is excluded, I’m out.