The (statistical) Truth About Cock Size

I agree with Priap. For many physical and mental traits, there are a greater number of outliers than would be predicted by a normal distribution. What constitutes an extreme outlier, however, is up for debate. Considering that I have only seen one guy in all of porn (Mandingo) who might possess a 10” dick, I doubt that there are many such anomolies out there. Eight and nine inchers, on the other hand, may be a bit more common than indicated above.

I’m not so sure of the, “If it’s long, it must be thick” (or vice versa) crowd. People have claimed a correlation; that those with naturally long lengths will be more likely to be thicker. Is this scientifically backed at all? It seems from the data we cannot tell.

Sorry, I just have to ask this agian. Why are the percentiles completely different for girth than the other percentiles for the same data (as reported by mr. average, etc.)? Is it because you made a bell curve which you admitted already shouldn’t work? Just seems like an extreme change.

Wesley pipes. You see a member thats like 9-10” probably closer to 9”, but has a 5” or so on the girth.

There are long and thins, just like short and thicks. I don’t think either way is more common.

A girl I knew, complained about a guy who she described as “being almost as long as a ruler”, she said it was not much thicker than a “finger”.

She couldn’t “feel anything” and it only “hurt” when it “poked” her.

The issue at hand is one of covariance versus absolute independence, i.e. do girth and length relate to each other at all? The method by which Supersizeit treated the volume interpretation, which he pointed out was a rough approximation, would be found if girth and length had perfect covariance, e.g. for each one inch in length the girth increases by 0.75 inch. Thus, a 10”x7.5” penis would have a probability of 1 in 3.5 million. This is quick, but a bit dirty.

The other end of the issue would be one of absolute independence, i.e. girth has no relation whatsoever to length. Mathematically, we would treat this as (1/x)*(1/x), that is 1 in x multiplied by 1 in x. To use Supersizeit’s example:

(1/3,500,000) * (1/3,500,000) = 1 in 12,250,000,000,000 or one in twelve-and-a-quarter trillion.

Okay, that seems really unlikely. The truth is somewhere in the middle - girth and length tend to vary together, but not perfectly. I would hazard a guess that most guys will find that their girth and length are within one to two standard deviations of each other. Mine are within 2.12 std dev of each other. There are going to be some guys who will fall outside of this paradigm and have either a really long, skinny penis or a really short, but fat penis, but I imagine that they are, again, pretty rare.

I have not looked extensively at the Mr. Average or the Wikipedia data (which apparently was lifted from the Mr. Average site), but I am assuming that those analyses were based on the raw data, i.e. the person(s) who tabulated and graphed the data had access to each discrete measurement. I only have access to the mean and standard deviation information, so my analysis is based exclusively on the means and standard deviations. That is why it is different from their analyses.

Was that clear? It is the difference between one person being told that six items cost $1, $2, $2, $3, $5, and $6, while another is told that six items were purchased with a mean price of $4.66 +/- $1.04 (that is an example, I did not actually figure the S.D.). The person with the discrete numbers can interpret the data differently than someone who has access to just the mean and S.D., which is why I was jonesing for the actual data set earlier today. Was that clear?

I missed my edit deadline :(

I just went and looked at the Mr. Average graphs, and yes, the difference between the percentile information in my analysis and the percent of men in each 1/4” bin on that graph is based on the presence of the complete data set to them and its absence to me. Basically, they were reporting some massaged data as histograms, whereas my analysis was a normal distribution (for length, anyway).

One thing that I note that bothers me is that the graphs percentages do not not jive with the reported sample size, which makes me wonder along with Para-Goomba if these graphs are really from the LifeStyles data at all. Here are some examples:

For the 5.75 to 6.00 inch bin the percentage is given as 23.9%, but with a sample size of 300 men, this percentage would suggest that 71.7 men in the survey fell into this length range. How can you have 0.7 of a man?

For the 8.50 to 8.75 and 8.75 to 9.00 inch bins the percentage is given as 0.1%, but based on a sample size of 300 men, each full man would be 0.33%. This suggests to me that the graphs are based on a different data set, or someone screwed up the graph.

The problem is that those Mister Average breakdowns are the Kinsey data (possibly mixed with some other crap in the length case). They are not the Lifestyles data. I wish people would stop linking to the Mister Average site in every thread about size, because its charts are clearly bogus.

Your proof above with regard to the sample size reinforces my note that the girth data breakdown is exactly the same as the Kinsey girth data breakdown (including the odd number of men who obviously measured their diameter). Thanks for that :up: I remember using the same technique to detect bogus data in another thread.

Err, and of course Priap is right that even if the Mister Average charts were from the Lifestyles data, they still wouldn’t correspond exactly to his calculations, since data on something like this are never perfectly normally distributed.

For anyone following along, my points are not disputing anything Priapologist has said in this thread… just railing against an annoying website that keeps popping up in threads about average size :)

Para-Goomba,

Well spotted on that Kinsey data. I hunted it up and there are some striking similarities between Kinsey’s self-reporters and the purported “LifeStyles”-sourced graphs on the Mr. Average web site, right down to the quarter-inch-interval binning of the data. It appears that someone shifted data between bins to smooth the data, making it into an approximately normal distribution, but they left tell-tales, like the upper and lower tails and the 23.9% at 6.00 inches. Looks like fabrication to me.

In order for 23.9% and 0.1% to work, barring rounding errors, the sample population would have had to have been 1000 or multiples of 1000, not 300 as was obtained in the actual LifeStyles study.

Decimal equivalent of 23.9% is 0.239, 1000*0.239 = 239
Decimal equivalent of 0.1% is 0.001, 1000*0.001 = 1

So, I do not trust the Mr. Average graphs either, as they look like massaged repackaging of Kinsey data.

However, everyone, the means and standard deviations that I used were from an Ansell/LifeStyles website, so I still stand by them and the resultant analyses.

>>>However, everyone, the means and standard deviations that I used were from an Ansell/LifeStyles website, so I still stand by them and the resultant analyses.

As do I. :) Sorry about mucking up this thread a little.

So has the data been shown for EG? I am at 4.5 what are the chances of having that? Is it in normal range? Or does it have like dependent factors such as the chances between having a 6inx4.5in vs. 5inx3.5in, type deal? Basically whats the normal EG range?

Ugh, this is crazy :homer3d: .

Approximately 1 in 6

Very much so. You are less than one standard deviation below the mean.

No.

The erect girths of 95% of all men in the US fall between 3.956 and 6.132 inches.

Awesome! Thanks for answering all the question.