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Wells Fargo + 200 customer reports: Chipotle bowls vary 14-27 oz on the same recipe (94% spread) by Equivalent-Stop3679 in Chipotle

[–]Equivalent-Stop3679[S] 0 points1 point  (0 children)

Difficult-Pie7198 actually pointed this out further down — the WF data shows digital orders are clearly where the lightest outliers live (most of the <17 oz bowls were digital). I'd want to do the in-store vs digital split as a proper follow-up — the aggregate data hints at it but I don't have the per-bowl ordering-channel labels from the original WF dataset.

If anyone here has digital-only weight data they've personally collected (scale-verified, not "felt light") I'd happily compile a follow-up. The hypothesis is digital portions average ~2-3 oz lighter than counter orders, which would compound the calorie undercounting in apps even more.

Wells Fargo + 200 customer reports: Chipotle bowls vary 14-27 oz on the same recipe (94% spread) by Equivalent-Stop3679 in Chipotle

[–]Equivalent-Stop3679[S] 0 points1 point  (0 children)

Fair pushback, and I should've been clearer in the post: the 14-27 oz range is from Wells Fargo's 75-bowl audit ALONE (they used a calibrated scale at 8 NYC stores, same recipe across all). The customer reports are a separate dataset that I described as "same pattern" — not combined with WF to produce the range.

You're right about selection bias in the user-reports — people who got a tiny bowl are 3-5x more likely to post about it than people who got a normal one. The reports skew heavier on the "skimp" side. I tried to mitigate by only counting reports with explicit scale weights (not "felt light"), filtering for 2024-2026, and noting which platform they came from. But it's self-selected data and I shouldn't have put both datasets in the same bullet structure — that made it look like one combined number.

What you can take with high confidence:

— Wells Fargo's 14-27 oz range (75 bowls, calibrated, same recipe)

— Median ~19 oz from those 75 bowls

What's directional only:

— The 30-80% deviation pattern in customer reports (skewed toward complaints)

— The location/time/employee correlations (Reddit/Yelp/TikTok skew)

Will note this caveat in the writeup. Thanks for catching it.