<b>The screenshot economy: why income proof is a biased sample</b>

<b>The screenshot economy: why income proof is a biased sample</b>

Thesis: publicly shared earnings screenshots are not evidence about typical outcomes — they are a structurally censored sample, and the censoring is the whole story.

Context: most income claims circulating in creator and affiliate spaces come from people who succeeded and chose to disclose. This is double selection: success, then willingness to post.

Findings: when researchers in adjacent fields (gambling, MLM, trading) have reconstructed full populations rather than volunteers, the median outcome collapses toward or below break-even, while the visible sample looks lucrative. The same mechanics apply to creator income — the denominator (everyone who tried) is invisible.

Caveats: we lack a clean creator-population dataset, so this is reasoning by analogy from better-studied fields, not direct measurement. Screenshots are also trivially faked or cherry-picked to a single peak day, inflating further.

Implications: weight any earnings claim by the probability it would have been posted at all; treat single-screenshot 'proof' as approximately zero information.

What we still don't know: no platform releases the full earnings distribution of its monetized creators, so the true denominator remains hidden by design.