How to Conduct a Crosstab / Chi-Square Test
Use a chi-square test or crosstab when you want to measure the relationship between categorical variables.
Step 1: Access the Chi-Square Test Tool
- Select Crosstab in the sidebar menu.
Step 2: Select Variables for Analysis
- Enter your categorical variables with categories.
- Example: select Churn (no churn, churn) and Location (urban, suburban, rural).
Step 3: Configure and Run the Test
- Review the contingency table generated by the platform (counts for each combination of categories).
- Review:
- The chi-square statistic
- Degrees of freedom
- p-value
Step 4: Interpret the Results
- Check the p-value:
- If p < 0.05, there is a statistically significant association between the variables.
- If p ≥ 0.05, there is no significant association.
- Review the contingency table to see which categories contribute most to the association.
Step 5: Report and Act on Insights
- Look for Averisys AI and click Results to view the summarized findings:
- Clearly state whether an association exists between the variables.
- Highlight any practical implications (e.g., customers with low satisfaction are more likely to churn).
- Develop action plans based on insights (e.g., target interventions for high-risk groups).
- Click Reports to see the executive summary.
- Click Publish this page to share results with stakeholders via a published page.
- Click Invite Users to invite stakeholders to share results via dashboards.
Example Interpretation
- A significant result between Churn and Location suggests that churn rates differ by location.
Tips & Best Practices
- Only use the chi-square test for categorical variables.
- Ensure each cell in the contingency table has a sufficient count (ideally ≥ 5).
If something does not work as described
Screens are updated as the platform changes, so a button may sit in a slightly different place than the wording above suggests. When that happens, the AI assistant inside the platform is the fastest way forward: it can see the page being worked on and answer in context, at any hour. Ask it what a message means, why a file was rejected, or which step comes next.
Most problems in this part of the workflow come from the data rather than the platform. Column headers that repeat, numbers stored as text, blank rows at the bottom of a file, and inconsistent category labels are the usual causes. Checking those four things resolves the majority of failed uploads and unexpected results before any further troubleshooting is needed.
For anything that still does not resolve, email support@averisysanalytics.com with the guide title, the step reached, and what appeared instead of the expected result. Including the file name and a screenshot usually means the issue is answered in the first reply. Related instructions are listed in the full platform guide, and common questions are collected in the support FAQ.