Does the flat pattern hold up within individual categories, not just across size groups?
Checking the spread of the six-or-more-names rate across the 188 individual category groups measured, to see whether the aggregate figure is representative or an average masking large swings.
class_spread_across_groups.names 6 or more.median_pct
An aggregate percentage can be misleading if it is an average of wildly different individual cases. It is possible, in principle, for the overall figure that 684,307 of 752,124 answer sets (91%) named six or more distinct businesses to be true while individual categories vary enormously, some clustering near zero and others near total, with the overall number just landing in the statistical middle without describing any real category well. This finding checks that possibility directly by looking at the spread across individual groups rather than the pooled total.
The study measured 188 individual groups (categories) and recorded, for each, what share of its answer sets named six or more distinct businesses. Across those 188 groups, the median share was 91.8%, meaning half of all groups had a six-or-more rate at or above that figure and half fell below it. The lowest group recorded a rate of 65.3%, and the highest recorded 98.3%.
That is a real range, not a point. A group at 65.3% behaves quite differently from one at 98.3%, and a company operating in a low-rate category should not assume the overall 91% figure describes its own situation. But the range, while real, is still concentrated well above the halfway point. The minimum of 65.3% is still a majority, not a near-zero outlier, and the median of 91.8% sits close to the overall pooled rate of 91%. Contrast this with the mirror-image spread for the five-or-fewer class, where the median across the same 188 groups was only 8.3%, with a maximum of 34.7% and a minimum of 1.7%. In other words, even the group least likely to get a rich, six-or-more answer still produced one at a rate of 65.3%, well above the rate for terse, five-or-fewer answers in that same group.
So the flat pattern across size segments is not an artifact of averaging opposite extremes into a misleading middle. Most individual categories, whatever their size, sit in a similar band, tilted toward naming six or more businesses rather than five or fewer. The variation that does exist, from 65.3% to 98.3%, is worth investigating on its own terms, since something is driving individual categories toward one end or the other. But this study, built around category size as the explanatory variable, finds that size is not that something, since the spread does not track cleanly with the size segments already examined. A category near the low end of the six-or-more range is not reliably a small category; it could as easily be a large one with some other property, unmeasured here, that produces terser answers.
Other findings
When an AI engine answers a question about a small, niche market category, does it name fewer distinct businesses than it would for a huge, well-known category?
Do small categories get fewer named businesses than large ones?
A look at whether category size predicts how many distinct businesses an AI engine lists in its answer, across four size segments spanning from the smallest to the largest quarter of the sample.
Does the share of answers naming many businesses rise steadily as category size increases, or is it flat across all size groups?
How consistent is the naming pattern across all four size segments?
Checking whether the six-or-more-names share moves in a gradient from small to large categories, or stays essentially level across all four segments.
With market sizes varying so much, could a handful of dominant categories be driving the aggregate numbers rather than the pattern holding broadly?
Could one or two huge categories be skewing this whole result?
Checking whether the flat naming pattern across category sizes depends on a few outsized groups, or whether it holds because no single group dominates the sample.
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