Priesthood, Pedophilia & Homosexuality

  • At most 22 percent of catholic priests in the USA are homosexuals.
  • Homosexual men in the USA, as a group, molest children at a rate at least 15 times higher than heterosexual men.

One  asteroid rubs Earth, a meteorite crashes on Russia and, a few days later, Pope Benedict XVI took the cosmic message and resigned. Nonetheless many people question the true reason for his resignation alleging that it has nothing to do with fatigue but rather with homosexuality networks within the Church (CNN guest claiming a 50% of homosexuals among priests) and unresolved pedophilia scandals. So I took a look at this percentage with our best friend when it comes to politically incorrect statistics; Bayes’ Theorem, and I got the results displayed above.

pope_kiss

I know, I know, the numbers are pretty crazy, but they are based on data fetched from official sources and, before going into the details, let me play sociologist. Although homosexuals, as a group, molest children at higher rates than heterosexuals it is very important to realize that this does not necessarily mean homosexuals are more prone towards this behavior, assuming this might constitute an ecological fallacy, in this case it makes more sense that this outcome obeys to the fact that young boys are way less protected by parents than young girls and predators take advantage of this.

The Calculations

To estimate the rate of homosexuals among catholic priests we will first estimate how much more likely are male homosexuals to engage in pederasty compared to male heterosexuals, then we will use this result join with the by gender percentage of children abused by catholic priests (81 percent of the victims were males in the USA) to calculate the final figure.

I'll protect you Continue reading

There is no Theorem but Bayes’ and Laplace is His Prophet

As a student I thought that there was no fanaticism involved in the world of Mathematics. Sure in Science you always have crackpots and competing crazy theories around but I thought such things could not possibly happen with something so aseptic and precise as math. So you can imagine my surprise when I found out about this curious religious group in the field of Statistics who call themselves Bayesians.

there is no theorem but bayes

Bayesianism is a religion which demands its followers to use Bayes’ Theorem for any reasoning involving uncertainty regardless whether the reasoning is deductive or inductive in nature, though they also advice to consider more everyday life questions like Continue reading

Media on “Video Games & Violence”

video.games.study
                                           Click to watch video

This is the numerical result the researchers used to make their “ridiculous” assumptions in their paper:

The ANOVA procedure for repeated measurement designs yield significant results for the

  • dACC (Wilk’s Λ = 0.33, F = 4.59, p <.027, η2 = 0.67)
  • rACC (Wilk’s Λ= 0.19, F = 9.55, p <.003, η2 = 0.81)
  • amygdala (Wilk’s Λ = 0.28, F = 5.75, p<.014, η2 = 0.72)

Tests for linear trends were significant in the three ROIs:

  • dACC: F = 8.28, p < .014;
  • rACC: F = 17.97, p < .001;
  • amygdala: F = 30.02, p < .001

but not for higher order trends

The other study they mention does not involve any experiment and is merely a review of other studies.

Whether the significance in the study is significant for science is up to the researchers but, yeah, we can make assumptions with a sample size of just 13. Interestingly, others would regard “too many” people in a sample size as a manipulation to achieve significance. So I guess that when we don’t like something we can always find reasons to complain about it.

Climategate (3/3): Fear mongering

The last post in this Climategate series is dedicated to the climate of fear mongering we all see every now and then in the media claiming extreme weather patterns linked to global warming in an end-of-the-world tone. I will offer some insights and calculations to show that “extremist” might be wrong.

So let us begin with the Australia Climate Change? New Colors Added To Forecast Maps news that spread like… well.. wildfire not just in Australia but through all over the world.

o-AUSTRALIA-CLIMATE-CHANGE-570

It seems that poor climatologists in Australia had no choice but to reuse the purple color already in use for the negative range (-25, -18) ºC for the positive range (50, 54) ºC. What could possibly have done these people but to mix cold and hot weather colors!? well, here’s an idea:

Not Scary Colors for Australian Temperatures

There it goes a present for climatologists in Australia; 121 not scary-oh-my-gaw-how-hot-it-is different colors for the range (-60,60) ºC, and just in case you need more I have a few spare millions. You’re welcome.

Okay, okay, to be fair Continue reading

Climategate (2/3): be careful what you model for because you might get it

In the previous post I showed how James Hansen at GISS NASA clearly over estimated global warming in the late 80’s due to the modeling choices he made. To make a point on how influential the choice of a model is, in this post I will make modeling choices that will allow us to claim that global warming can be explained as a fluke in a random process.

I like to explain the relationship between data and models saying that data is the shadow reality casts, and models are what we believe is casting the shadow. So once we have a model  we can use it to cast shadows (make predictions) like the one James Hansen did and could be read in 1986 newspapers:

Hansen predicted global temperatures should be nearly 2 degrees higher in 20 year. “Which is about the warmest the earth has been in the las 100,000 years.”

Interestingly James Hansen downgraded his prediction in a 1988 paper from the nearly two degrees higher to a one degree higher. Though to be fair I would not be surprised if media misquoted him; I might not trust scientists but I absolutely distrust media.

Anyhow, let’s now compare NASA’s prediction in this 1988 paper (in red) to what actually happened years later (in blue):

Yearly Average Global Temperature Change
1988 NASA predictions on top of yearly average global temperature changes with major volcano activity and parts per million levels of CO2

Data for this plot comes from the B.E.S.T and N.O.A.A.

Continue reading

Climategate (1/3): be careful what you model for because you might get it

Deception is all around us, in every little parcel of our life; from our personal Bart Simpson’s “It wasn’t me” to our local TV news host selling us the latest “You’re not going to believe this” but we eventually do. One might just wish there would exist communities out there with higher standards like, for example, Christians priests but, nope, they cover up pedophile networks in order to preserve The Church’s “good” name. But how about the atheist priests a.k.a scientists? How about their standards?

Well, unfortunately the community of scientists might have more to do with priesthood than one might expect or desire, and a nice example of this would be the Climategate (or the Climatic Research Unit email controversy, as some people had the kindness to rename the Climategate article in the Wikipedia following Fox News’ motto “fair and balanced” )

So in this post I am going to replicate earlier studies on global warming to uncover how over pessimistic were the maths models of the past, but I will also talk about human weakness, and scientists are human… for now.

Mike's Trick
Click to watch Dr. Richard A. Muller (Professor of Physics at the University of California at Berkeley) talk about “Mike’s Trick” and the Climategate.

Continue reading

My Baby Shot Gun Control Me Down… with Statistics

Best way to lie? Statistics, no question about it, and if you don’t believe me I can show you some statistics that support this point… or any other point for that matter. But statistics are even better to lie to yourself; the same way different people see different things when they look at clouds, people also see different things when they look at data, and if there is one endless debate where people reinforce their beliefs with cherry picked data that debate is gun control.

tyt.us.vs.japanTalking about cherry picking, let’s check at the The Young Turks‘ argument to support stricter gun control in the USA: “ban guns like Japan and you have 2 gun related homicides, don’t ban guns and you have 10,225 gun related homicides”. And they explain their point in a way that you’d better not to dare to disagree. Sure they could have compared data from 2007 where the USA had 9,146 gun related homicides and Brazil 34,678 mentioning that Brazil has much tighter gun control laws than the USA but, unfortunately, this is the way politics and media works; it does not matter if you are right or wrong, only if you look right or wrong. But let’s analyze some data and a few more examples of how information is presented to us. Continue reading

Astrology: The woo-woo that works

French psychologist Michel Gauquelin gained notoriety in the 50’s after publishing data showing that sportsmen were born in a non random fashion when considering the movement of planet Mars, the nicknamed Mars Effect has been the core of passionate discussions over its statistical validity since then but, beyond whether this effect truly holds or not, there are many reasons why genuine statistically significant data can be found in the astrology world, so we’d do better not to ignore planets and stars entirely.

mars_effect
Number of sportsmen born given the position of planet Mars according to Gauquelin’s data

We humans develop efficient strategies in our daily life that are useful for most situations, for example, if we see dark clouds and a few moments later it rains we associate dark clouds with rain and, voilà, next time we see dark clouds we take measures. The problem begins when we break a leg right after seeing a black cat, our association machine, a.k.a brain, does its magic and next time we see a black cat we take measures too. But you know what? The brain is right!

What is not so right is human difficulties to removed associations once they are set in our brains, a.k.a stubbornness. We humans develop all sort of strategies too keep our associations alive and demand extraordinary amounts of evidences to break them yet, even when those evidences are presented, we keep fighting them by doubting the methodology or the honesty of the persons bringing them up. There might be evolutionary advantages explaining why we create associations so easily but cannot break them with the same ease, but whatever the reasons are the problem only worsens when in some cases our stubbornness makes the associations come true! And that is what astrology is all about. These are a few examples of how astrology makes spurious associations come true: Continue reading

Anal Sex and Smoking… Or How Dangerous Danger Is

  • Anal sex increases your chances to have anal cancer up to 17 , 31 times.
  • Smoking cigarettes increases your chances to have lung cancer 23 times.

Yet, though you probably want to drop smoking, homosexual men should not worry too much about anal sex. Women, on the other hand…

marlene_dietrich_smoking

So yeah, Merry Christmas and have a nice day… But wait a minute, before you rethink your lifestyle and become a monk or a nun to avoid a premature death, let’s see how dangerous danger is. Continue reading

Racial Profiling vs Description of the Suspect

You are a policeman in a car chase of a criminal wearing globes and a mask, the most likely scenario according to statistics is that the criminal is a white person. Then the car stops in front of a bar and the criminal rushes in getting rid of the globes, mask and changing his clothing. You enter the bar and you see a white guy and a non-white guy. Who should you question first? The non-white guy. Racism? No, Bayes’ Theorem.

reverse-racial-profilingAccording to the US Department of Justice racial profiling is defined as:

Any police-initiated action that relies on the race, ethnicity, or national origin rather than the behavior of an individual or information that leads the police to a particular individual who has been identified as being, or having been, engaged in criminal activity.

A key part in this definition is where it justifies the police-initiated action when there is information that leads to a particular individual. In other words, if there are witnesses saying that the thief was a barefooted blond white little girl wearing a green blouse and a red tutu then going after girls looking like that would not be considered racial profiling but simply checking on the description of the suspect.

But how about if the police-initiated action is not based on information coming from witnesses but in information coming from statistics? Is information coming from statistics still information according to the definition of the US Department? Continue reading