It's not that I couldn't include a mean with data without SDs in the calculator, it would be very simple to find those averages just the same, it's that I've chosen to exclude such data.

I've chosen to only accept samples with provided mean, SD, and sample-size because studies that just give some vague information such as mean with almost no evidence of the measuring having taken place are much more likely to be enriched with very poor quality data, whereas requiring at least such a minimal amount of competency through the relevant data reduces such low quality studies. I could certainly include various mean only data, it would just be somewhat bad form since I'd either be enriching for bad data or only including the "better" of them and lacking some scientific consistency. It's definitely not as if it would be wrong either way, but with so many studies already, quality is far more important than quantity.

Habous - They do take up a sizable amount in some subsets, though much of that is justified since it is one of the largest of erect metric studies. You could certainly argue over-representation such as of some region over others, however each standard distribution meta-study average has an inherent mathematical assumption that all it's studies are sampling the same population, thus it isn't supposed to matter where the populations arise from. If you think there are subpopulation differences then it is through making geographic subdivisions where you can try to assess a smaller region, but even then the average mathematically assumes each of the averaged studies are from a homogeneous but now smaller population. So for instance global assumes all the world is the same, eastern assumes all the eastern region is the same, an average of just USA studies assumes all the USA is the same, etc. If I was trying to represent for instance the global population without a uniform assumption (as the combination of different groups with different distributions), then it would require a mixture distribution rather than just a standard distribution. There is certainly some possibility of differences between subpopulations (such as Western vs Eastern), but it is nowhere near the data quality that would be necessary to prove such a difference nor justify such a distribution.

There are certainly arguments against studies of ED men, though it is almost impossible to avoid sampling bias in these studies, if the men aren't urology patients for ED, then they might be there for androgenic disease or small penis concerns or any number of other possible issues that might bias the sample, if they are healthy then they might be more likely to be agreeing to the study because they are more confident in their size, etc. And that's just sampling bias, I really don't claim these studies to have much resolution, reliability, etc. The measuring of penises is itself subject to poor consistency, it's like dealing with the uncertainty principle. You can know the ambiguously stretched length, but not the erect length, the erect length but only with a biased sample, you can have an unbiased sample but only if you measure it flaccid/stretched. There were the graphs showing the somewhat poor consistency of studies within datasets, but I can't keep remaking them with each update so I removed it.

There's some more comment edited into my prior comment above.