Running and Verifying Chi-Square Tests in GraphPad Prism: A Step-by-Step Guide The Chi-square ( χ2chi squared
This is arguably the most critical assumption, and it is one that . Each subject in your study must contribute independently to the contingency table. Independence means that the outcome for one subject does not influence the outcome for any other subject in any way. If you are combining data from two different clinics, two different hospitals, or two different experimental batches, you are likely violating this assumption. In such cases, you need more advanced statistical tools such as logistic regression (available from Prism 8.3 onward) to properly account for the clustering.
: Input your data into the grid where rows represent groups (e.g., treatment) and columns represent outcomes (e.g., pass/fail). Do not use normalized values . chi square graphpad verified
: Not statistically significant. You fail to reject the null hypothesis. Chi-Square Statistic ( χ2chi squared ) and Degrees of Freedom (df)
Tells you if the association is statistically significant (usually Chi-square value ( χ2chi squared ): The calculated statistic. Degrees of Freedom (df): Calculated based on table size. Running and Verifying Chi-Square Tests in GraphPad Prism:
Measures the strength of the association. 3. Key Considerations for Verified Results
Ideal for showing how the relative proportion of outcomes changes across your experimental groups. Use the Format Graph options to add asterisks (e.g., ** for If you are combining data from two different
The Y-axis represents the raw frequency or percentage of outcomes within those groups.
: You must enter the exact raw number of subjects or events . Never input normalized values, fractions, averages, or percentages. Prism requires raw integers because the underlying calculation is fundamentally dependent on actual sample size. GraphPad Prism 11 User Guide - Contingency tables
Each subject must contribute to only one cell in the data table. Repeated measures on the same subject cannot be analyzed with a standard Chi-square test.
“A significant association was observed between treatment type and patient recovery (χ²(1) = 6.48, P = 0.011, odds ratio = 2.3, 95% CI: 1.2 to 4.5).”