Skip to main content

Project Snow White

Hello internet, and welcome to The Science They Don't Want You to Know.  As I have mentioned in the first post of this series, I am doing research regarding the statistical viability of currently unconfirmed conspiracies (no leaked documents) by way of currently known conspiracies (documents have been leaked).  The primary purpose of this initial research is to gather particular information, specifically how many people were involved in the actual conspiracies and the length of time which these conspiracies took place.  If you have not read the first post, you should read it here.

With all of the revealed conspiracies I have covered so far in this blog, you would think that they are only performed by government agencies, that no other organization could or would pull off such acts of deceit and maliciousness.  After all, what organization has enough people who would perform actions which would fall under the category of conspiracy?  The government pays it's employees to perform a wide variety of actions, and everyone on a government has a clause in their contract which makes them open to perform actions of conspiracies.  What other organization can say that?

The "Church" of Scientology, apparently.

We all know full well that Scientology is filled with a bunch of wackos.  If you don't know that, there is a plethora of documented cases of them just going crazy.  There are more videos proving this than there are words in this blog post, so I can not link to all of them.  I can, however, link to one unique video per word in this paragraph and let you be the judge.  Overkill?  Yes.  Why?  Because I am a scientist, and I want to provide proof to everyone I can of all of my claims, and I see statistical analysis as the pinnacle of physical proof.

After all, that's why I am writing this series; to perform statistical analysis on known conspiracies to look at the viability of supposed conspiracies.  While statistics cannot absolutely prove a concept to be true, with enough data points, we can have a high confidence of its truth.

So those in Scientology tend to act crazy.  With that in mind, the FBI and CIA have been observing them for quite a while.  Their files on the "religion" grew substantially large.  This has come after the IRS has determined that they are not a religion worthy of Tax-Exempt status.  After all, Scientology was a growing national security threat to the powers that be in the government.  But that's not the conspiracy I'm looking at here.

When the powers that be in the "Church" of Scientology found out about this, they kind of proved the point of the government.  The higher ups of the church developed a plan to scrub the government of all documentation labeling Scientology in a negative light.  Once the plan was hashed out and the pieces were put in place, Project Snow White was executed.

The plan was to have numerous members of the Church of Scientology infiltrate the government in all sectors with observations of Scientology.  They would then coerce the organizations to stop observing Scientology, then purge all documents referencing the "Church".  The plan commenced in 1973, which is the start date for this project.  For a fringe group, they had a good deal of success in infiltrating the government, the 5,000 operatives destroying a good 70% of files related to the group before getting caught five years later.  They were eventually caught in 1977, and their case went to trial in 1978.

So that's four years where the conspiracy was active for the 5,000 people who perpetrated it.  With that in mind, for those of you keeping track, that gives the propability of failure per person per year of 3.8059E-05, bringing the average to 1.2290E-04 and the standard deviation to 2.9526E-04.

Relevant Entries:
First
Next
Labels:

Comments

Post a Comment

Popular posts from this blog

Basic Statistics Lecture #3: Normal, Binomial, and Poisson Distributions

As I have mentioned last time , the uniform continuous distribution is not the only form of continuous distribution in statistics.  As promised, here are the three most common continuous distribution types.  As a side note, all sampling distributions are relative to the algebraic mean. Normal Distribution: I think most people are familiar with the concept of a normal distribution.  If you've ever seen a bell curve, you've seen the normal distribution.  If you've begun from the first lecture of this lecture series, you've also seen the normal distribution. This type of distribution is where the data points follow a continuous curve, is non-uniform, has a mean (algebraic average) equal to the median (the exact middle value), falls from highest probability at the mean to (for all practical purposes) zero as the x-values approach $\pm \infty$, and therefor has equal number of data points to the left and to the right of the mean, and has the domain of $(\pm \i
1 comment
Read more

Confidence Interval: Basic Statistics Lecture Series Lecture #11

You'll remember last time , I covered hypothesis testing of proportions and the time before that , hypothesis testing of a sample with a mean and standard deviation.  This time, I'll cover the concept of confidence intervals. Confidence intervals are of the form μ 1-α ∈ (a, b) 1-α , where a and b are two numbers such that a<b, α is the significance level as covered in hypothesis testing, and μ is the actual population mean (not the sample mean). This is a the statement of there being a [(1-α)*100]% probability that the true population mean will be somewhere between a and b.  The obvious question is "How do we find a and b?".  Here, I will describe the process. Step 1. Find the Fundamental Statistics The first thing we need to find the fundamental statistics , the mean, standard deviation, and the sample size.  The sample mean is typically referred to as the point estimate by most statistics text books.  This is because the point estimate of the populati
1 comment
Read more

Basic Statistics Lecture #5: Baye's Theorem

As promised last time , I am going to cover Baye's Theorem. If Tree diagram is the common name for Bayes Theorem.  Recall that conditional probability is given by $P(A \mid B) = \frac{P(A \wedge B)}{P(B)}$.   For tree diagrams, let's say that we have events A, B 1 , B 2 , B 3 , … (the reason we have multiple B's is because they all are within the same family of events) such that the events in the family of B are mutually exclusive and the sum of the probabilities of the events in the family of B are equal to 1. Then we have $$P(B_i \mid A)= \frac{P(B_i)*P(A \mid B_i)}{\sum_{m=1}^{n}[P(B_m)*P(A \mid B_m)]}$$  What this means is reliant on the tree diagram. If we are only looking at the sub-items of A, this is what the tree diagram would look like. If J has a probability of 100%, and P(C) and P(D) are not 0, then when we are trying to find the probability of any of the B's being true given that A is true, we have to set the probability of A to be the entir
Post a Comment
Read more