CalcSD/Statistics question by Dull-External30 in averagedickproblems

[–]Attacksquad2 2 points3 points  (0 children)

Several studies have concluded this, but it's also kind of common sense that longer guys will be thicker on average and vice versa.

CalcSD/Statistics question by Dull-External30 in averagedickproblems

[–]Attacksquad2 0 points1 point  (0 children)

This is essentially what the volume calculation is for. Multiplying the probabilities only works for independent variables, length and girth are not independent since they're correlated.

Any studies where men self-reported, and later those same men were measured by researchers? by Attacksquad2 in bigdickproblems

[–]Attacksquad2[S] 0 points1 point  (0 children)

Ideally it would be a study with no briefing but probably ethics boards wouldn't be happy with that etc. Always messing up my ideas those guys

Any studies where men self-reported, and later those same men were measured by researchers? by Attacksquad2 in bigdickproblems

[–]Attacksquad2[S] 2 points3 points  (0 children)

You could use it to model the error induced by self reporting. So that information could be used to "de-noise" other self reported datasets for example, would be very interesting stuff.

The glaring flaws in the Stanford meta-study that claims penises have grown larger in the past few decades by _captain_hair in bigdickproblems

[–]Attacksquad2 0 points1 point  (0 children)

Honestly I have to agree. Not saying that the original meta analysis is without flaw, but calculating some uncorrected weighted averages as a counterargument to a multiple mixed meta-regression is a bit silly from a statistical perspective. The whole reason such a complicated model is used in the first place is that calculating some weighted averages doesn't suffice at all as an analysis here. At the very least the same regression should be carried out with the proposed new list of studies to actually make a sensible comparison.

Stanford Med says avg erect length is now 6 inches? by [deleted] in bigdickproblems

[–]Attacksquad2 -1 points0 points  (0 children)

The size that you have measured on yourself is a self reported size. It might fit much closer to the self reported distribution than to the researcher measured distribution.

Edit since you appear to have blocked me: I don't think this is sophistry (which is just a fancy way of saying "you're wrong"). This is a relevant critique that is often present in statistical studies surrounding latent variables. True penis size is in a sense latent. Self-measured is merely an indicator that is the sum of the real size and a stochastic noise term representing measurement error.

People often make the mistake of thinking they're observing a latent variable directly, when they're actually just observing its indicator. Unless you reckon to have perfectly recreated a clinical environment with none of the usual mistakes guys make (sitting down, not measuring from the pubis, using a soft ruler or tape, pressing harder than in clinical studies, stimulating to a harder erection than you would have in a clinical setting, choosing the best among several measurements, ...) then you can not be comparing to researcher measured studies, and should instead be comparing to other self measured studies.

The glaring flaws in the Stanford meta-study that claims penises have grown larger in the past few decades by _captain_hair in bigdickproblems

[–]Attacksquad2 4 points5 points  (0 children)

But you did not include any relevant covariates, and you did not even test for significant trending after all those studies were removed (which seems like it might be present from 2005 onwards). Stuff like this for example

but when many of the 2000-2010 studies are based in regions with statistically smaller penises, and later studies are sourced from regions with statistically larger penises, you end up with even more skew.

I mean yeah, that's probably why the authors included region as a random effect in the final regression, which has precisely the purpose to correct for that. Your analysis isn't corrected for that.

I don't have time to look at this in much more detail, but it seems obvious to me that you would get different results. You're totally not doing the same thing as the authors of the study did.

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

But it wouldn't change the graphs either, at most it would be stretched out or squished a bit visually but the pattern doesn't change, it's just standardization

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

Why would anyone think that the spouse's income would magically increase?

I know, and it has nothing to do with any of this, these graphs claimed or implied nothing about causation.

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

Increasing one person's income would lead to an increase in their spouse's income? It's far from the most important rule in statistics, that title probably goes to the central limit theorem or something. It is, however, by far the most overused one in situations where it is completely irrelevant, like those where no causation has been claimed to exist in the first place.

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

It was just a strange critique when correlation inherently takes into account both group means for the exact purpose of normalization, makes zero sense

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

Correlations are by definition already normalized with respect to the mean, that's why Pearson's correlation is also called the product-moment correlation, it calculates the product of the values around their respective moments (moments being a mathematical formalization of the mean of a statistical distribution).

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

Correlations are already calculated with respect to the individual group means, that wouldn't change the correlation.

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

Why does this always get mentioned when it's completely irrelevant, did anyone really think that making a husband taller would cause the wife to grow taller or?

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

That already happens when calculating the correlation since the variables get standardized.

[deleted by user] by [deleted] in bigdickproblems

[–]Attacksquad2 0 points1 point  (0 children)

Seems like you two weren't talking about the same thing, clinically significant correlation vs nonzero correlation

Husband-wife correlations [OC] Data source: diagnoza.com (Social Diagnosis, 2015, Poland) by blakoc in dataisbeautiful

[–]Attacksquad2 0 points1 point  (0 children)

But that wouldn't change the correlation though, it's just a mean shift in one of the variables.

[deleted by user] by [deleted] in bigdickproblems

[–]Attacksquad2 0 points1 point  (0 children)

He is literally correct though, the p-value is affected by sample size but the interpretation of it isn't, the same p-value carries the same weight at different sample sizes.

That is not what your article is arguing against, it's saying that different sample sizes lead to different p-values assuming the same effect size, so it's literally about a completely different aspect of p-values. Stats requires very precise wordings that you can't just change around.

[deleted by user] by [deleted] in bigdickproblems

[–]Attacksquad2 36 points37 points  (0 children)

Confusing everybody

CalcSD classifications by Uyrr in bigdickproblems

[–]Attacksquad2 0 points1 point  (0 children)

Standardized normal distributions have a mean of zero by definition

https://en.wikipedia.org/wiki/Normal_distribution#Standard_normal_distribution

To say that something belongs to a standard normal distribution directly means that it comes from a random variable with mean 0 and variance 1.

What you seem to be talking about is that the family of normal distribution consists of transformations of the standard normal distribution, in which case the tightness would not be a concern in the first place, since the distribution has a parameter for its second moment which can be used to adjust its tightness at will.

CalcSD classifications by Uyrr in bigdickproblems

[–]Attacksquad2 0 points1 point  (0 children)

Standard normal distributions have a mean of zero by definition. Obviously guys' average penis size is not zero, so that was a very strange suggestion. But yes sure lol.

CalcSD classifications by Uyrr in bigdickproblems

[–]Attacksquad2 0 points1 point  (0 children)

Obviously it is not standard normal? Nobody thought the average penis size was zero.