A statistical proof of God?

by Ali-Asad

"I could prove God statistically. Take the human body alone - the chances that all the functions of an individual would just happen is a statistical monstrosity."
~George Gallup

In this quote, George Gallup hints at the probabilistic proof of God. This proof argues that the chance our universe evolved from the big bang to the way it is today are extremely minuscule. Roger Penrose, Oxford scholar and contemporary of Stephen Hawking, popularized this argument in his book The Emperors New Clothes. Here, Penrose calculated the probability of that the universe evolved into what it is today from all the possible random outcome. This figure he calculated to be;
1 in 10¹⁰¹²³.
That's an infinitely small probability; 0.000(+10,120 more zeros).

But can we put forward a more basic argument for the existence of God based on the science of statistics? How about the fact that the probability that a continuous random variable takes an exact value is equal to exactly zero? Let me explain.

Firstly, what is a continuous random variable? A random variable is just anything we can measure that varies; e.g. the number of cats in each home for the district you live in. In one house there could be 2 cats; in another 0, in another 4 and so forth. This random variable is an example of a discrete random variable. Discrete just means the random variable takes exact values (you can't have half a cat, or a quarter etc). A continuous random variable would be a measure of something like temperature or height. These don't take specific exact values but in practice we usually round them off for simplicity and that makes them discrete.
So far we've defined a random variable as a measure of something that varies and there are two types; discrete and continuous.

How do we find the probability that a continuous random variable takes an exact value? Imagine we define our (continuous) random variable as the height of all children in a school. Is there anyway to understand how these heights vary? Yes; the good ol' bell curve. But how the bell curve came about is important. It was actually discovered by a dude called DeMoivre; he found the bell curve was a good approximation for the binomial distribution when (n) was large.

Say what?

My post on Understanding Margin of Error describes what I'm talking about; but I'll go over it again. If I were to go up to one of the children in this school and ask them; "is your height 5 feet?" they would respond yes with a probability of (p) where (p) is the % of all school going children in the population of height 5 feet. This is the Bernoulli test. Now if I ask this child more questions like "Are you 4 feet tall?" and so on; eventually I will ask them (n) questions trying to deduce their height. If I could ask them an infinite number of questions (statisticians are just cool like that), I'd get a perfect picture of how tall this child is. This action of making (n) large (asking a lot of questions in the example) results in the bell curve or normal distribution. This fact hints at the power of the bell curve; you find it everywhere in nature.

Now we have a distribution that will help us find the probability a random child in this school is less than 5 feet, greater than 5 feet or exactly 5 feet. Let's try to find the probability that the child is exactly 5 feet and let's imagine that the average height of all school children is 4 feet. How do we find this probability. Firstly, we know that the total area under this curve must be 1 other (the sum of all the probabilities for each probable value must equal 1). So we have to find the area under the curve using what, calculus? Heck no. We can approximate the area under the curve with a rectangle (calculus is just cool like that).
Area of rectangle = length * width
Our length is 5 feet and our width is...0! Ouch.
Therefore the probability that our child is exactly 5 feet is exactly 0, as is the probability for any exact value.

Now the 64 million dollar question is; Given the above, how is it that I (and you too) have an exact definite height? The probability of us having an exact height is. Additionally, the bell curve applies to many other measures such as weight. I weigh an exact amount but should I? Can I? In his book God Hypothesis, Michael Corey makes a similar but less rigorous argument using the constants of nature. He argues that these constants that make the universe work could have taken an infinite range of values and so the probability that they have this one perfect value is in fact 0.

In other words, the probability of the word around us existing like we see it is exactly 0. Does that make for an effective argument for the existence of God? Or does that make the science behind beginning of the universe even more interesting?
Perhaps both.

And that's jus' the tip

Comment below.

References

http://search.barnesandnoble.com/booksearch/isbninquiry.asp?ean=9780641706011&z=y

The Normal Distribution, Wikipedia

Normal Distribution, West Virginia University

http://www.amazon.com/God-Hypothesis-Discovering-Goldilocks-Universe/dp/0742520544