Checkbox Distribution
Checkbox Distribution shows the true/false ratio for each checkbox field, helping you spot values that are rarely toggled, or skewed in an unexpected direction.
The Checkbox Distribution analysis scans checkbox (boolean) fields, and calculates the percentage of TRUE and FALSE values across all records. A ratio far removed from 1.0 points to a heavily skewed checkbox that may no longer be serving its intended purpose.
Use this analysis to find checkboxes that never vary, are ignored by users, or should be driven by automation but aren't. Run it to confirm that checkbox fields are actually being used as intended.
Checkbox Distribution can evaluate any field typed as a checkbox (boolean) at the metadata level, including system-managed fields such as Converted, Deleted, or Unread By Owner.
Detailed Job Results
The True/False Ratio per Field bar graph shows for each analyzed checkbox field its percentage of TRUE and FALSE values. Hover over a TRUE or FALSE section to see more details such as the exact percentage and number of records. The chart view exposes fields with extreme distributions, so you can quickly spot the checkboxes that might need attention.
The Field Details table below displays for each field the total number records with a value for this field ( # Records with Value), the number and percentage of records with True or False as value ( # Records with True, % Records with True, # Records with False, % Records with False), and the True/False Ratio.
A TRUE/FALSE ratio of 1.0 points represents an even distribution of 50%/50%. A ratio far removed from 1.0 points to a heavily skewed checkbox that may no longer be serving its intended purpose.
Note that the ratio scale isn't symmetric around 1.0. A field skewed toward FALSE compresses toward 0 (bounded between 0 and 1), while a field skewed toward TRUE can rise arbitrarily high (39, 100, 1,000...) depending on how rare the FALSE values are.
Key Insights
- Constant fields: Checkboxes that are 100% TRUE or 100% FALSE carry no informational value and are candidates for removal or deprecation.
- Near-constant fields: Checkboxes where one value accounts for the large majority of records (for example, 98% FALSE) often indicate a confusing label, an unchanged default, or a process that no longer uses the field as intended.
- Automation gaps: Flags that should be updated by automation but show little variation may indicate the automation isn't running or was never implemented.
Recommended Actions
| Scenario | Actions |
|---|---|
| Field is 100% TRUE or 100% FALSE | - Confirm with the stakeholder that the field is no longer meaningful. - Plan removal once you've confirmed no integrations depend on it. |
| Field is heavily skewed but not constant | - Check whether the label is unclear or misleading. - Check whether the default value is rarely changed by users. - Rename, add help text, or adjust the default so the field reflects a real choice. |
| Preparing data for reporting or AI | - Exclude constant and near-constant checkboxes from dashboards and standard reports. - Exclude these fields from AI features and scoring models. - Focus models and reports on checkboxes that actually vary. |