Statistics & results interpretation
Most statistical problems in a thesis are interpretation problems. These guides explain p-values, confidence intervals and effect sizes in plain language, how to choose a test, and how to report results without producing a list of p-values.
Choose the right test, then read and report the output in a way that answers your research question.
- Which statistical test fits my variables?
- What does p > 0.05 actually mean for my study?
- How do I report results so reviewers can follow them?
Start here
How to Choose the Right Statistical Test Without Memorising a Table
Stop choosing tests from a giant table. Start with the research question, outcome, comparison, dependence, design and effect estimate you actually need.
Read guideKey guides
What Does p < 0.05 Mean? A Simple Guide to P-Values
What does p < 0.05 mean? Learn what a statistically significant p-value tells you, what it does not tell you and how to report it correctly.
6 min readP > 0.05: Does It Mean There Is No Effect?
A p-value above 0.05 does not prove there is no effect. Learn how to interpret non-significant results using effect estimates and confidence intervals.
7 min readHow to Interpret a 95% Confidence Interval
Learn how to interpret a 95% confidence interval, what its width means, why the null value matters and how to report confidence intervals correctly.
7 min readP-Values, Confidence Intervals and Effect Sizes: How to Read Them Together
P-values, confidence intervals and effect estimates answer different questions. Learn how to read them together and interpret results without reducing everything to significance.
8 min readStatistically Significant Does Not Mean Important: How to Judge Whether a Result Matters
A small p-value does not tell you whether a finding matters. Learn how to judge clinical, practical and programmatic importance using effect size, uncertainty and context.
6 min readHow to Report Quantitative Results Without Turning Your Results Section Into a P-Value List
Write a quantitative results section that tells readers what you found, not just which p-values crossed 0.05. Includes practical before-and-after examples.
7 min readHow to Choose Variables for a Quantitative Study Before You Open SPSS
Choose variables from your research question and causal logic, not from a list of demographics or bivariate p-values. A practical workflow for quantitative studies.
8 min readIndependent, Dependent, Confounding and Mediating Variables: What They Actually Do in a Study
Learn what exposure, outcome, confounder and mediator mean using one research example, and why a variable's role depends on the causal question.
8 min read
Tools and templates
See everything for this stage: what to learn, use, download and check.