> ## 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*

```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
```


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