MX8's built-in statistical robustness allows researchers to interpret their data confidently without extensive statistical knowledge.
By automatically highlighting significant results and filtering out less reliable data points, MX8 enables researchers to focus on deriving meaningful insights rather than managing statistical complexities.
The platform highlights significant insights within your survey data. All results in reports are color-coded for quick interpretation:
Green: Significantly higher result
Red: Significantly lower result
Blue: Normal result, no significant difference
Transparent: Result is below the reporting threshold (default minimum: 20 respondents)
By default, MX8 uses a residual t-test at a 95% confidence level, filtering out cells with fewer than 20 respondents. This approach is ideal for general usage as it balances accuracy and simplicity. It identifies results significantly higher or lower than expected based on a linear distribution of responses, making it straightforward to spot unexpected or unusual patterns.
MX8 also provides multiple statistical tests, allowing users flexibility to select the most appropriate method based on their research goals, data characteristics, and analytical needs:
Why might I want to change from the defaults?
To conduct more granular analyses within smaller subsets of data.
If a different confidence level (e.g., 90% or 99%) is required to align with specific research standards or regulatory guidelines.
To perform comparative analyses focused explicitly on rows or columns rather than overall data distribution.
Adjusting the Confidence Level
Confidence testing identifies results that are statistically significant compared to other responses. The default confidence level in MX8 is 95%, indicating there's only a 5% chance that the marked differences happened by random variation.
When to change:
When a higher confidence level (e.g., 99%) is necessary for stricter confidence in findings.
When lower confidence levels (e.g., 90%) can highlight potential trends or insights for exploratory analysis.
Using a Residual T-Test
This approach identifies cells within a report table significantly higher or lower than expected based on a linear distribution between rows and columns. It is handy when pinpointing unusual patterns or deviations from typical response distributions.
When to use:
This is for standard exploratory analysis or to identify unexpected anomalies in the data.
If a more focused comparative analysis across rows or columns is required, consider using a row—or column-based t-test.
Using a Row-Based T-Test
This test compares each cell against other cells in the same row. Cells marked with labels indicate those cells in the same row that are statistically lower.
When to use:
When analyzing category performance within individual rows.
Applicable when assessing item-level differences clearly across horizontal data.
Using a Column-Based T-Test
This test compares each cell against other cells in the same column. Labels indicate that cells in the same column are statistically lower than those highlighted.
When to use:
It is ideal for vertical comparisons across segments or groups.
When identifying standout categories across columns is a priority.
Changing the threshold for Reporting
MX8 applies a reporting threshold to each response. If the number of respondents for a specific cell falls below this threshold, the results are not colored in the reports.
This is applied at the level of the individual response to the question, not at the question itself, so that we can filter out significantly low responses to an asymmetric question. For example, let's ask people about their ethnicity. We’re more likely to get statistically significant results for common ethnicities. Still, we don’t want to filter out ethnicity options entirely because of the small number of respondents in a specific minority.
The default threshold for reporting is 20 respondents.
When to change:
Increase the threshold if you require more robust, high-confidence insights.
Lower the threshold if your analysis requires greater sensitivity to smaller subpopulations or niche segments.