Skip to main content

Patreon

So, as you know, I have been trying to get statistical analysis of known conspiracy theories (including MK Ultra, The Tuskegee Experiment, and the NSA Spying Program) for the series called The Science They Don't Want You to Know.  This is to perform statistical analysis on supposed conspiracies, such as global climate change, vaccines, and GMO's.

In order to fully and thoroughly explain the science and stats, I need to get my hands on science behind pay walls.  And I need your help for that.  You can help by becoming Science of Life patron: go to http://www.patreon.com/thescienceoflife314 to find out how.

Comments

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...

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 po...

The Connections Between the Sciences

I apologize for taking so long with this entry of my blog. I have been abnormally busy lately with my academics and poetry. Today, I am writing on how all of the sciences are related to one another, in the hopes that you will come to realize that the sciences are not as separate as popular culture and news has us believe. This blog will be geared to those individuals – weather you're the average person or a student of science, or a full blown scientist – who have the opinion that the different fields of science are completely isolated from one another. This sentiment is not true, and I hope to show the false-hood of this concept here. In physics, we have the concept of “The Right-Hand-Rule”. This pretty much determines whether the a force perpendicular to two vectors is “positive” or “negative”. Torque is a good example of this. The amount of torque placed on, say, a bolt by a crescent wrench is perpendicular to the position vector and the fo...