Statistical test selection
Which Statistical Test Should I Use?
A practical overview of common statistical tests, data structures, and key considerations for research studies.
Read articleRESOURCES
Statistical Resources for Research
Explore clear, general introductions to common study questions, data structures, and statistical methods.
Comparing group means
T-test vs. ANOVA: How to Choose for Comparing Group Means
Learn when to use a t-test or one-way ANOVA, how the methods relate, and when follow-up comparisons are needed.
Read articleStudy design
Paired vs. Independent Samples: How Study Design Changes Your Statistical Test
Learn how paired and independent samples differ, why the distinction matters, and how it changes a two-group comparison.
Read articleStudying associations
Pearson vs. Spearman Correlation: Which Measure of Association Should You Use?
Learn when Pearson or Spearman correlation is appropriate, how linear and monotonic relationships differ, and what to inspect before interpreting a correlation.
Read articleCategorical data
Chi-square vs. Fisher’s Exact Test: How to Choose for Categorical Data
Learn when chi-square or Fisher’s exact test is appropriate for categorical data, how expected counts affect the choice, and what to report.
Read articleStatistical foundations
What Is a Normal Distribution? A Practical Guide for Research Data
Learn what a normal distribution is, how to recognize it in research data, and when a bell curve is not the right model.
Read articleStatistical foundations
How Hypothesis Testing Works: A Practical Guide for Research
Learn how null and alternative hypotheses, p-values, confidence intervals, and statistical significance fit together in research.
Read articleStatistical foundations
How to Interpret P-Values and Confidence Intervals Together
Learn how p-values and confidence intervals answer different questions and how to report results beyond statistical significance.
Read articleStudy planning
Type I and Type II Errors, Power, and Sample Size
Learn how false-positive and false-negative risks, power, and sample size fit together when planning a research study.
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