The Stat-o-Sphere is an open educationaleditorial collection within BEM that provides low-threshold, easy-access and slow-paced statistics tutorials for absolute beginners ─ written by students, for students. Our goal is to evoke and elevate both research and reviewing abilities within readers and are eager to further expand our peer-teaching spaces within BEM together with you:
In the future, we will also provide you with educational content as an optional extension of submissions within BEM, giving students the chance to share their method with us (especially concerns submissions providing open data and code).
Most of our tutorials will also be accompanied by executable code ─ so far for the open access programming language R; others such as python, SPSS, STATA or Matlab will follow. Learn more about our project and its goals in our introductory article “Into the Stat-o-Sphere” below.
We are currently looking for reviewers / test readers. Contact us via [email protected] if you want to participate. Every reviewer will be credited with a mention at the beginning of the tutorials.
Inferential Statistics is our first tutorial series and aims to provide thorough tutorials on fundamental methodological basics in statistics on three levels of perspective and representation: intuition/conceptuality, mathematics and computer science (currently in R for computers with 64-bit architecture).
This series will also be the thorough basis for providing additional educational material for other peer-teaching formats within BEM, such as the review crash course and the educational journal club (JCed).
Part I introduces the basic concept of probability and the scientific logic behind probabilistic inference (e.g., p-values). We also provide a general overview on paradigms in statistics: Bayesianism and Frequentism. This tutorial also entails a very basic introduction into R: how to install R, how to open a script, how to execute code…
Part II presents the linear least square method to obtain the parameters (slope and intercept) of an optimal linear model. This tutorial also introduces the concept of dependent and independent variables and discusses the difference between probabilistic and statistical modelling. We also reproduced the complete math behind the basic lm(y~x) function in R and learn how to write our own functions.
