On this page, I will aggregate issues related to statistics proper. For a developed resource page on mixed regression models, go here. This page will cover facts about other statistics – effect sizes, bayesian analyses, homo/heteroskedasticity, etc. Given the need to run high powered studies to output worthwhile science, I will also be collecting links and papers here regarding: how to run power analyses, the concepts behind them, simulations, and general information about them along with issues of replication and reproducibility of analyses.This page is currently under construction.
Error management
 Familywise error rate: Link
T Test
Degrees of Freedom
Testing Variance homo/heterogeneity
 Understanding Ftest to compare variances (R’s var.test)
 Compare Multiple Sample Variances in R
 Variance homogeneity in two or more groups
Effect sizes
 On standardized or unstandardized Effect sizes: Link
 Interpreting Cohen’s d effect size
 Abandoning standardised effect sizes and opening up other roads to power

Five different “Cohen’s d” statistics for withinsubject designs
NHST
 Null Hypothesis Testing: Link
Moderation/Mediation
Confidence Intervals
Likelihood statistics
Multivariate methods
Correlations
 Interpreting Correlations
 Visualizing Dendrograms in R
 Reproducing lattice dendrogram graph with ggplot2
 Correlation ‘distances’ and hierarchical clustering

PCA, MDS, kmeans, Hierarchical clustering and heatmap for microarray data
 Not another heatmap tutorial
 Correlation matrix : A quick start guide to analyze, format and visualize a correlation matrix using R software
 correlation matrix to build networks
 What techniques exists in R to visualize a “distance matrix”?
 Diagonal labels orientation on xaxis in heatmap(s)
 Adding a Dendrogram to a ggplot2 Heatmap
Bayesian
Power
 Power analysis through simulation:
Links: [tutorial1] [tutorial2] [for mixed models] [conceptual steps]  On the need for power analysis: LinK, Link2
 GLIMMPSE
Reproducibility
 The following site is a tutorial for how to structure your analysis stream to make it more reproducible. Link
Measurement
Replication
P Hacking/QRPs
 The Grad Student Who Never Said “No”
 Example of what not to do.
Machine Learning
 Machine learning in neuroscience: Link
Signal Detection Theory
PCA & EFA
 Principal Components and Factor Analysis
 Exploratory Factor Analysis Theory and Application
 Principal Component Analysis vs. Exploratory Factor Analysis
 Lavaan Project

The Fundamental Difference Between Principal Component Analysis and Factor Analysis
 Exploratory factor analysis in R
SEM