The significance of Thunder's Place PE Data stats

Is just a mistype or I’ m misunderstanding something?

However, 50% of probabilities to gains 0.5” EL in 6 months - 1 year (if one stays committed) is somewhat vets was agreeing, so our personal experience and statistics seems to be consistent enough (that’s the surprisingly thing :) ).

Do we assume that these statistics are inflated due to self measurements as is the case with other surveys(durex, kinsey). The spokesman for lifestyles said that 75% of men are below the average that kinsey reported. Which was even lower then what was reported for durex.

Also, are the statistics on volume taken using the BPEL measurements?

That’s right, there is a listing in the PE data for a 13” guy. So technically, I guess that is that maximum :)

I am thinking about this aswell. However, I think that most people here probably want to be honest against themselves when it comes to measurements. I for one know that I don’t want to fool myself by measuring and then adding a number to that measurement when I am trying to keep track of my gains. Since the Thunder’s PE data site mostly is made for oneself and not for “showing off”, it seems likely to me that most people would want to enter measurements as accurate as possible.

I think the biggest problem with the PE data site is that people simply measure in a wrong way. I know I did in the beginning and that gave me an extra 0.25” that I didn’t have. It was a big let down when I noticed that I had done wrong, but at the same time it felt good because I had acquired more motivation to keep going :) .

Nope, the row shows: starting length= end length= 13”; so the gain should be zero.

Yeah, this was one of the data sets that I did minimal filtering of bad data on. I hate to do too much manual manipulation because that introduces my bias into what would otherwise be independently collected data. However, there are numerous entries that are clearly .

Oh, I see what you are saying. No, theses rows and columns are not calculations of themselves. They are independent statistics for the entire data set.

So the max starting length and max ending length for the inclusive data set could have belonged to two different guys. In this case, they belonged to the same guy (“joey-styles”) who entered the largest entry, but never reported growing. The max change in length belonged to “hardjoker” and “frostyguypa.”

If this were the exclusive data set, each contributor has made multiple entries, which usually means they’ve increased, but several reported now change or a decrease in one or both dimensions.

Oh, thanks.

ok, so I didn’t read this whole thread so it may have been mentioned. Did you remove repeat entries from TP data? we’re all growing here unlike the other measurement studies. So use 1st entry by a user only?

In the most recent entries focused on growth, I was excluding entries from contributors that only made one entry. I call this the “exclusive” set. The “inclusive” set includes the entries from the guys that only made single entries, as well.

There are a couple of ways to do this exclusion, but in the data I’ve shared just considers the first entry and the last entry. Another way that doesn’t look much different, takes the first entry and the max measurements from any of the entries. I don’t like that as much since it includes measurements that were at least somewhat temporary.

I’ve been busy, so I’m sorry for the lack of posts on this thread.

The rank of growth in circumference is another very good perspective on the TP PE data, and comes in handy when setting goals and maintaining a healthy perspective about the possibilities of PE.

|| rank ||| ∆C ||| || rank || %∆C | | days
|100.0% | -0.75 | |100.0% |-10.7%| | 1
| 97.5% | -0.13 | | 97.5% |-2.3% | | 11
| 95.0% | 0.000 | | 95.0% | 0.0% | | 20
| 92.5% | 0.000 | | 92.5% | 0.0% | | 28
| 90.0% | 0.000 | | 90.0% | 0.0% | | 31
| 87.5% | 0.000 | | 87.5% | 0.0% | | 36
| 85.0% | 0.000 | | 85.0% | 0.0% | | 46
| 82.5% | 0.000 | | 82.5% | 0.0% | | 56
| 80.0% | 0.000 | | 80.0% | 0.0% | | 61
| 77.5% | 0.010 | | 77.5% | 0.2% | | 68
| 75.0% | 0.062 | | 75.0% | 1.2% | | 77
| 72.5% | 0.100 | | 72.5% | 1.8% | | 90
| 70.0% | 0.100 | | 70.0% | 2.0% | | 99
| 67.5% | 0.125 | | 67.5% | 2.4% | | 116
| 65.0% | 0.125 | | 65.0% | 2.6% | | 124
| 62.5% | 0.125 | | 62.5% | 2.8% | | 136
| 60.0% | 0.185 | | 60.0% | 3.6% | | 149
| 57.5% | 0.200 | | 57.5% | 3.9% | | 164
| 55.0% | 0.200 | | 55.0% | 4.3% | | 183
| 52.5% | 0.250 | | 52.5% | 4.5% | | 207
| 50.0% | 0.250 | | 50.0% | 4.9% | | 225
| 47.5% | 0.250 | | 47.5% | 5.0% | | 245
| 45.0% | 0.250 | | 45.0% | 5.3% | | 276
| 42.5% | 0.250 | | 42.5% | 5.6% | | 302
| 40.0% | 0.300 | | 40.0% | 6.1% | | 331
| 37.5% | 0.320 | | 37.5% | 6.8% | | 365
| 35.0% | 0.375 | | 35.0% | 7.3% | | 388
| 32.5% | 0.375 | | 32.5% | 7.9% | | 423
| 30.0% | 0.400 | | 30.0% | 8.5% | | 468
| 27.5% | 0.450 | | 27.5% | 9.1% | | 510
| 25.0% | 0.500 | | 25.0% | 9.8% | | 583
| 22.5% | 0.500 | | 22.5% |10.3% | | 653
| 20.0% | 0.500 | | 20.0% |11.1% | | 731
| 17.5% | 0.562 | | 17.5% |11.6% | | 844
| 15.0% | 0.625 | | 15.0% |12.5% | | 957
| 12.5% | 0.700 | | 12.5% |14.3% | | 1089
| 10.0% | 0.750 | | 10.0% |15.8% | | 1308
|| 7.5% | 0.875 | || 7.5% |17.6% | | 1691
|| 5.0% | 1.000 | || 5.0% |21.1% | | 2491
|| 2.5% | 1.250 | || 2.5% |26.3% | | 3831
|| 0.0% | 2.750 | || 0.0% |63.3% | | 5344

To reiterate, I’ve thrown out the entries from contributors that only made a single entry because it is hard to speculate about why they only made one entry. I call the resulting set the “exclusive” set because it excludes the single entries. Fortunately, adjusting for those entries is pretty simple because they account for just over half of the entries in the db. If you want to include them in your interpretation of the data, simply cut the percentage value in half for any given rank. The relative and absolute values then become zero between the negative entries and the rest of the zeros; effectively accounting for 95-45% on the scale.

I’ve also included the duration rank in days for an interesting point of reference.

At the top 50% rank, you see 0.25” absolute growth, ~5% relative growth, and 225 days duration.
At the top 25% rank, you see 0.50” absolute growth, ~10% relative growth, and 583 days duration.
At the top 10% rank, you see 0.75” absolute growth, ~15% relative growth, and 1308 days duration.

Basically, you see fewer and fewer guys attaining that higher levels of growth and it taking longer and longer to do so. It reflects what we know, but provides some real numbers about how many guys have done it and how long it takes.

Everyone grows at different rates, but later on I plan to show a prediction model that would give someone a confidence interval for growth given a commitment to a routine of a certain duration. Without a more rigorous calculation, you can provide an intelligent answer to the quintessential newbie question “is it possible to grow X in Y weeks?”

If the question is: “Can I grow 0.25” in 7 months?”, the objective answer would be: “probably”, as in more likely than not, before going into “Everyone is different, depends on your body, commitment, etc”

Since the question is usually: “Can I grow 1 inch in 3 weeks before I see my girlfriend again?”, besides scolding them with a firm “no”, you can add that “only 5% of the guys that tracked their gains here have ever grown an inch or more in girth.”

In addition, I can say that the guys in that category have been working at it for an average of 1067 days, or 677 median days. To be more specific, guys that grew to an inch, but not beyond average 880 days, with an 305 day median. The table above doesn’t tell you those numbers, but Thunder’s PE data does expose that data. The table does illustrate pretty clearly that you can gain a little in a relatively short amount of time, but substantial gains usually take a long, long time.

There is another way to slice the data, factoring time in a more granular way (instead of focusing on a contributor’s “career”) that should provide more insight, and I will eventually get to that, too.

A million thanks Slipstream. Great service to the community!

So, on average it takes about between two and a half and three years to gain an inch in girth. I’ve got my goals set on trying to gain 1.25” in girth, and before I saw this thread, I calculated that this will probably take around three years, and now that’s more or less confirmed. Well ,that last 0.25” will - on average - amount to a total of 3.65 years of PE. So here’s 44 total months of PE waiting for me!

More graphs for the penis statistics nerds. It’s been six years since slipstream compiled the percentiles, so I’ve updated the graphs to reflect all the new data entered in the size database since then.

First is the inclusive starting length percentiles. I removed about 70 data points that either didn’t contain length or girth, or had what I considered to be impossible starting size (10x1, 9x2, for example). I also removed about 20 entries with starting size 1x1 or smaller. What remained were 7208 starting stats, what slipstream called the “inclusive” data set. Meaning that it includes all members who entered at least 1 data point.

I reported the raw data in the “TP Data” column and the “Starting L” graph. Some highlights from that data:
9.0” is the 99.6% mark.
8.0” is the 96% mark.
7.5” is the 89% mark.
7.0” is the 76% mark.
6.5” is the 56% mark.
6.0” is the 34% mark.
5.5” is the 13% mark.
5.0” is the 3% mark.
4.0” is the 0.4% mark.

That data has a median length of 6.375”, and an average length of 6.41”.

These statistics overestimate the real world measurements, mainly because of two biases: the self measurement bias, and the previous PE bias. In other words, as a whole we tend to measure ourselves a little bigger than we would be measured in a scientific study. And since we are a PE community, there are guys who had already gained before entering their first measurement in the database.

Apparently those two biases add up to about 0.375” of extra length on average, because the consensus of scientifically measured studies is that the average length in the non-PE world is about 6.0”. So I made a second column of data, and a second graph which simply shifted the entire dataset downward by 0.375”, making the new median 6.0”, and hopefully reflecting the real world population pre-PE starting size, for our comparison obsessing needs.

Some highlights from that data, which includes a (-0.375) in the titles:
9.5” is the 99.9% mark.
9.0” is the 99.8% mark.
8.0” is the 99.0% mark.
7.5” is the 95% mark.
7.0” is the 87% mark.
6.5” is the 71% mark.
6.0” is the 50% mark.
5.5” is the 25% mark.
5.0” is the 10% mark.
4.5” is the 3% mark.
4.0” is the 0.9% mark.
3.0” is the 0.1% mark.

Clarification, I removed about 70 total data points, 20 of which were 1x1 or less. I also want to add that I think both columns are useful comparison tools. If you think you’re subject to the self measurement bias and are giving yourself a little extra with self-deceptive measuring tricks, the TP data set is a good comparison tool. If you think you’re getting an accurate measurement, the same a researcher would get, then the (-0.375) data set is a good comparison tool.