Blog
Practical guides on choosing, running, interpreting, and reporting statistical tests for your research.
How Many Participants Do You Really Need?
Determine the right sample size for your dissertation. Learn rules of thumb, power analysis basics, and what to do when your sample is too small.
Cronbach's Alpha Explained: When to Use It and How to Interpret It
Learn what Cronbach's alpha measures, how to interpret it, and when to use it for survey reliability in your dissertation. Includes common benchmarks.
SPSS vs. R vs. Excel: Which Should You Use for Your Research?
Compare SPSS, R, and Excel for dissertation research. Learn the pros, cons, and best use cases for each statistics tool to pick the right one for you.
When Your Data Violates Assumptions: What to Do Next
Learn what to do when your data violates statistical assumptions like normality, homogeneity, or independence. Practical solutions for dissertation research.
Understanding p-Values Without Losing Your Mind
A plain-language explanation of p-values for graduate students. Learn what they mean, what they don't mean, and how to interpret them in your research.
The Difference Between Statistical Significance and Practical Significance
Understand the critical difference between statistical and practical significance. Learn why a p-value alone doesn't tell you if your results matter.
Power Analysis for Your Dissertation Proposal: A Step-by-Step Guide
Learn how to conduct a power analysis for your dissertation proposal. Step-by-step guide covering G*Power, effect size, sample size, and alpha level.
Effect Size: What It Is, Why It Matters, and How to Calculate It
Learn what effect size is, why it matters more than p-values, and how to calculate Cohen's d, eta-squared, and more for your dissertation research.