Yes, you can define multiple anomaly detection tests (such as different configurations of elementary.column_anomalies) on a single column. This allows you to monitor different metrics or use different settings for the same column. For example, you might want to monitor both null_count and standard_deviation with different sensitivities. To differentiate these tests in reports and alerts (including Slack), you can add a custom description using the meta field in your test configuration. This description will appear in the report under each test result and in alerts, making it easier to distinguish between them. Here’s an example:

yaml columns:   - name: my_column     tests:       - elementary.column_anomalies:           column_anomalies: [null_count]           meta:             description: "Null count anomaly detection for my_column"       - elementary.column_anomalies:           column_anomalies: [standard_deviation]           meta:             description: "Standard deviation anomaly detection for my_column"

The description you set in meta will be shown in the report and in Slack alerts, helping you quickly identify which test triggered the alert. Note that the name or alias fields are not currently supported for this purpose in Elementary anomaly tests, so using meta.description is the recommended approach for differentiation in the UI and alerts [Give names/descriptions to tests](https://slack.com/archives/C02CTC89LAX/p1736818351146109), [Label/alias anomaly test chart](https://slack.com/archives/C02CTC89LAX/p1705372587987189), [Multiple versions of same test](https://slack.com/archives/C02CTC89LAX/p1697653387970259). Let me know if you need an example for a specific test type!