# My collection of reads on specific statistical problems

• Common statistical mistakes you should avoid (link)
• What makes a statistical analysis wrong? (link)
• P values in small sample sizes (link)
• the common usage of a Welch type t-test (link)
• A good review of the terminological uncertanties concerning dependent  / independent variables (link)
• Wilcoxon significance with similar means (link), check out the ancient median test (wikipedia)
• Alphas, P-Values, and Confidence Intervals: Oh My! (link)
• What is the Lan-DeMets approach to interim analysis? Calculation of corrected p-values for increased Type I error (link)
• Stop criteria for clinical trials: O’Brien–Fleming–type boundaries (link)
• Analysis of covariance (here and here)
• 6 Types of Dependent Variables that will Never Meet the GLM Normality Assumption (link)
• Spearman vs Pearson correlation for non-normally distributed data (link)
• Median vs mean: when the median doesn’t mean what it means (link) and the median isn’t the message (link)
• Shifting from hypothesis testing to prefferential techniques. “The new statistics.” Cumming (2014) Psychological science (PDF link)
• Understanding Bayes: A Look at the Likelihood (link) by Alex Etz
• Krzywinski M, Altman N. Points of significance: Power and sample size. Nat Meth. 2013 Nov 26;10(12):1139–40.
• Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. Nature Publishing Group; 2013 Apr 10;14(5):365–76.
• Statistical inference is only mostly wrong (link)
• Interpreting regression output in R
• Beginners Guide to Bayesian statistics (PDF)
• The confusing case of choosing x- and y-axes (link)
• General linear models: an introduction (link)
• Growth curve analysis in R (PDF)
• Generating a nested case-control cohort study using Epi in R (link)
• Understanding MatchIt output in R (StackExchange)
• Matching R package (paper PDF)
• Optmatch on CRAN (pdf)
• What is a propensity score (NCBI) and intro for observational studies (NCBI)