The column after the mean

A results table gives you a mean and, next to it, a second number that looks like a margin of error. That second number is not always the same kind of thing, and reading it as if it were produces a specific, common mistake: treating a narrow-looking interval as telling you about individuals when it is actually telling you about the mean.

Three different columns, one appearance

Standard deviation (SD) describes how spread out individual measurements are around the mean. It answers "if I picked one man from this sample, how far from the mean would his reading typically be." Standard error (SE) describes how precisely the sample mean itself is known, and it shrinks as the sample gets larger - a huge study can report a tiny SE even though individual men in it vary widely. Confidence interval (CI), usually reported at 95 percent, is built from the SE and states a range the true population mean is likely to fall within, not a range individual readings fall within.

All three can appear in a table looking like "mean (X)" with no more explanation than the column header, and the header is exactly what tells you which one you are looking at.

The relationship between SE and SD is arithmetic, not a matter of judgement: SE equals the SD divided by the square root of the sample size. That single fact explains why SE always shrinks as a study grows while SD does not - SD is a property of the individuals in the population, and adding more of them to the sample does not make any one individual less variable, it only pins down the mean more precisely. A table that reports SE without also giving SD, or n, leaves you unable to recover the individual-level spread at all.

Why the confusion matters

If a study reports a large sample with a tight 95 percent CI, it is easy to read that tight interval as meaning individual men cluster closely around the mean. They do not, necessarily - a huge sample narrows the CI around the mean regardless of how spread out the individuals actually are. The number that tells you about individual spread is the SD, and it is the one that matters for placing your own reading on a percentile, not the CI.

A small illustrative table

The two studies report nearly the same mean and a similar SD - individual men vary by roughly the same amount in both. The CI, though, is far narrower in the large study, purely because of its sample size. Reading the large study's tight CI as "individuals cluster tightly" would be wrong; the SD says they do not, and the CI was never claiming that.

Applying this to a real table

When you are reading a published table of size figures - Veale 2015 among them - check the column header before drawing a conclusion from the number in it. A percentile placement needs the SD, not the CI, because a percentile is a statement about where an individual sits among other individuals, not where the population mean sits. If a table gives you only a CI and no SD, it has told you the mean is well-estimated and said nothing about how spread out individual men are. A CI can, in a pinch, be converted back toward an approximate SD if the sample size is also given, since the CI's width is built from the SE, and the SE from the SD - but that is a calculation to do deliberately, not an equivalence to assume by eye, and it is worth doing rather than skipping when a percentile placement is the actual goal.

This distinction is unrelated to anything an image-based rating tool reports. A score from Rate Cock, a numeric rating site, a verdict from a human judge, or an estimate from an image-analysis model carries no SD or CI in this statistical sense at all, since none of them is sampled from a measured population the way these tables are.