> ## Documentation Index
> Fetch the complete documentation index at: https://docs.elementary-data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# ignore_small_changes

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```
ignore_small_changes:
  spike_failure_percent_threshold: [int]
  drop_failure_percent_threshold: [int]
```

If defined, an anomaly test will fail only if all the following conditions hold:

* The z-score of the metric within the detection period is anomoulous
* One of the following holds:
  * The metric within the detection period is higher than `spike_failure_percent_threshold` percentages of the mean value in the training period, if defined.
  * The metric within the detection period is lower than `drop_failure_percent_threshold` percentages of the mean value in the training period, if defined

Those settings can help to deal with situations where your metrics are stable and small changes causes to high z-scores, and therefore to anomaly.

If undefined, default is null for both spike and drop.

* *Default: none*
* *Relevant tests: All anomaly detection tests*

<RequestExample>
  ```yml test theme={null}
  models:
    - name: this_is_a_model
      data_tests:
        - elementary.volume_anomalies:
            arguments:
              ignore_small_changes:
                spike_failure_percent_threshold: 2
                drop_failure_percent_threshold: 50
  ```

  ```yml model theme={null}
  models:
    - name: this_is_a_model
      config:
        elementary:
          ignore_small_changes:
            spike_failure_percent_threshold: 2
  ```

  ```yml source theme={null}
  sources:
    - name: my_non_dbt_tables
      schema: raw
      tables:
        - name: source_table
          config:
            elementary:
              ignore_small_changes:
                drop_failure_percent_threshold: 50
  ```

  ```yml dbt_project.yml theme={null}
  vars:
    ignore_small_changes:
      spike_failure_percent_threshold: 10
  ```
</RequestExample>