Statistical Issues
Below are a number of websites to help with statistics.
Understanding Common Misconceptions about p-values
Problems with Small Sample Sizes
This blog discusses why small samples are problematic.
This blog discusses how low powered studies (even significant ones) can lead to wildly inaccurate effect sizes.
An investigation of the false discovery rate and the misinterpretation of p-values
This paper details the problems of using an alpha value of 0.05 and the word "significant".
This is a popular press version of the scientific article above.
The case for, and against, redefining "statistical significance."
Statistics: P values are just the tip of the iceberg
NHST and data analysis
Interpreting Cohen's D: An interactive visualization
An explanation of logistic regression, how and why to use it.
A practical primer for t-tests, correlations and meta-analyses
Four Misconceptions about Statistical Power
Power, the probability of finding effect, when there is one, is often confused.
One-Tailed vs Two-Tailed Testing
A discussion of using one- vs two-tailed tests.
Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant.
Statistical fallacies are common, but they can be avoided.
Understanding Statistical Power and Significance Testing
An interactive visualization for Type I, Type II error, B, a, p-values, power and effect sizes.