Quick answer: A chi-square test asks: are two categorical variables related, or is the observed pattern just chance? In jamovi: Frequencies → Contingency Tables → Independent Samples (chi-square test) — rows variable in, columns variable in, tick Chi-square, Expected counts and Contingency coefficient/Phi for effect size. Use it for infection × sex, treatment × outcome, smoking × diagnosis — any counts-in-cells question. The continuous-outcome counterparts live in our t-test walkthrough and ANOVA guide.
Table of Contents
Worked example: treatment and outcome
120 patients: 60 given a new therapy, 60 standard care; outcome recovered/not. The data must arrive as one row per patient (Treatment, Outcome columns) — or as pre-counted weights (Frequency Counts option). jamovi builds the 2×2 table; suppose χ²(1) = 5.22, p = 0.022, phi = 0.21.
Reading it: expected counts assume treatment and outcome are unrelated; the test measures how far observed counts stray from that. p = 0.022 < .05 → recoveries aren’t independent of treatment (the inference logic: our hypothesis testing guide). Phi = 0.21 is a small-to-moderate association — real, but clinically modest. Report as: “Recovery differed by treatment, χ²(1) = 5.22, p = .022, φ = 0.21.”
The three conditions chi-square demands
- Expected count ≥ 5 in every cell (or ≥ 80% of cells with ≥5 and none <1). jamovi shows expected counts in the Cells menu; if violated with a 2×2, tick the Fisher’s exact option instead.
- Independent observations — each subject appears once. Repeated measures on the same people need McNemar’s test, not chi-square.
- Counts, not percentages — the test is defined on frequencies. If your data arrive as percentages, go back to the raw counts.
Goodness-of-fit: the one-variable variant
The same menu handles one categorical variable: Frequencies → N Outcomes (χ² goodness of fit). Classic use: are births evenly spread across weekdays, or is a coin actually fair? jamovi compares observed counts to the proportions you specify (equal by default) and returns χ² and p. For ratio-level data and means, remember the categorical machinery here is deliberately separate from the means machinery in the ANOVA workflow.
Where chi-square sits in the biostatistics toolkit — and when design, not the test, is the problem — is covered in hypothesis testing in biology. For one-to-one help choosing tests for your own categorical data, Ampersand Academy teaches biostatistics with jamovi one-to-one.
Official documentation
For the full reference behind this guide, read the official jamovi site. It is the source the details above are drawn from, and the first place to check when an option or attribute name changes.
Frequently asked questions
When do I use a chi-square test?
When both variables are categorical and you have counts: infection by sex, treatment by outcome, grade by school. It tests whether the two variables are independent or associated.
What does an expected count below 5 mean for my test?
The chi-square approximation becomes unreliable. In a 2×2 table switch to Fisher’s exact test (one tick-box in jamovi); in larger tables combine sparse categories or collect more data.
How do I report a chi-square result in APA style?
Report the statistic, degrees of freedom, p and an effect size, for example: chi-square(1) = 5.22, p = .022, phi = .21. jamovi’s output contains every number you need.
What is the difference between chi-square and Fisher’s exact test?
Chi-square approximates the sampling distribution; Fisher’s computes exact probabilities from the hypergeometric distribution. Results are similar with large counts, but Fisher’s is the safe choice for small samples.
Can chi-square show how strong the relationship is?
Yes, via effect size statistics: phi or Cramer’s V for strength of association. A significant p with a tiny V means the relationship, while real, explains very little of the outcome.
Last updated on · Written by Dinesh Kumar R
