Hypothesis Testing (Non-Parametric) · TOOL GUIDE

Chi-Square Test

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Understand the method. Use it with confidence.

Test for association between categorical variables. Quantly runs the method against your active analysis context and returns structured results for review and interpretation.

01

When to use it

Use this tool for rank-, count-, or distribution-based comparisons when parametric assumptions are unsuitable or variables are categorical/ordinal.
Use it only when the selected variables and study design match the method's requirements.

02

How to use it

  1. Open the tool from the Quantly Tools panel.
  2. Confirm the active dataset and analysis session.
  3. Set Categorical Variable 1 (Required): Select the first categorical column.
  4. Set Categorical Variable 2 (Required): Select the second categorical column.
  5. Set Significance Level (α) (Required): The threshold for determining statistical significance.
  6. Set Generate Stacked Bar Chart (Optional): Visualize the relationship using a stacked bar chart.
  7. Set Generate Heatmap (Optional): Visualize the contingency table as a heatmap.
  8. Set Generate Mosaic Plot (Optional): Visualize the association using a mosaic plot.
  9. Review optional or advanced settings and keep defaults unless your design requires a change.
  10. Run the tool, inspect warnings and diagnostics, then save or export the result with its assumptions.
03

How to interpret results

Interpret the test statistic and p-value with an effect-size measure where available. A significant result indicates evidence of a difference or association, not its practical size or causal direction.