The Science of Life

This is an entry in the Basic Statistics Lecture Series which I have realized that I have failed to incorporate into the series so far, and I apologize for that.  This lecture is going to cover the basic calculations required for all of statistical analysis, on a fundamental level. When we collect statistical data, we typically collect it on a small part of the population as a whole.  The population is the entire group for which a sentiment holds true.  This small part of the population as a whole is called the sample of the population, or just sample for short.  The number of data points we collect -- the number of items in the sample -- is called the sample size, which is typically denoted by the letter n. In statistics, we typically calculate the arithmetic mean, which is the sum of all values divided by the sample size.  For the population as a whole, this is denoted by the greek letter μ (mu), and for a sample, it's denoted by x . The sample mean ...

As promised last time, today I will cover basic calculations of data accumulated from real data.  Please take note that all of the following is for the simple case of one group of data.  For two or more distinct groups of data, the calculations will be similar, but slightly more specific due to the nature of 2+ distinct groups of data.  I will cover that in a later post, which will be labeled as ANOVA.  As a side note, I'm a baseball fan, so I'm going to provide examples from the MLB. This information has the labels for the data of a sample, not the population.  The population is the set of all possible people or objects which falls under the category under study.  If we were studying the 2017 ERA's of pitchers, the population would be all MLB pitchers who have pitched in 2017.  The sample is the subset of the population which we are getting the data points from.  If we want to look at the 8 teams who have made it to the Division Series, then ...