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

# Dataset Statistics

> Understand how column-level metrics describe your data and power platform features

Dataset statistics are column-level metrics computed over the contents of a [dataset](/concepts/primitives/datasets). They provide a quantitative profile of each column—counts, bounds, distributions, and quality indicators—so you can understand what your data looks like without querying it directly.

## Why statistics matter

**Data quality assessment.** Statistics give you an immediate picture of data health. You can see how many values are null, whether numeric ranges look reasonable, and how complete each column is—all without writing a query.

**Schema understanding.** When working with a new dataset, statistics help you understand the shape of the data. Cardinality tells you whether a column has a few categories or millions of unique values. Histograms show you how values are distributed.

**Query context.** Statistics surface in the platform UI to help you make better decisions. For example, when filtering on a field in Data Studio, histogram statistics populate a dropdown with the field's actual values, so you can select from real data rather than guessing.

## Where statistics appear in the platform

Statistics are visible in two places:

* **Dataset details page** — shows column-level statistics for the full dataset
* **Dataset sample page** — shows statistics computed over the sample

They also power UI features elsewhere. In Data Studio, filtering on a field that has histogram statistics displays the histogram values in a dropdown for easy selection.

## Configuring what gets computed

You can control which statistics are computed, set refresh schedules, and add per-field overrides directly from the dataset details page. The configuration supports three levels of specificity: global defaults that apply to all fields, namespace-scoped overrides for dataset columns or Rosetta Stone attributes, and individual field-level overrides.

For step-by-step instructions, see the [configuring dataset statistics](/guides/platform/configuring-dataset-statistics) guide. For the full configuration schema and API details, see the [reference](/reference/architecture/dataset-statistics).

## What gets computed

The platform computes 12 statistics per column, grouped into three categories.

### Counts

Metrics that describe how many values exist and their uniqueness.

| Statistic             | What it tells you                                                                   |
| --------------------- | ----------------------------------------------------------------------------------- |
| `valueCount`          | Total number of non-null values                                                     |
| `nullValueCount`      | Number of null values                                                               |
| `nanValueCount`       | Number of NaN (not-a-number) values—applies only to floating-point columns          |
| `approxCountDistinct` | Approximate number of unique values, using a probabilistic algorithm for efficiency |
| `countDistinct`       | Exact number of unique values                                                       |

### Bounds and distribution

Metrics that describe how values are distributed across the column.

| Statistic           | What it tells you                                                |
| ------------------- | ---------------------------------------------------------------- |
| `lowerBound`        | Minimum value in the column                                      |
| `upperBound`        | Maximum value in the column                                      |
| `histogram`         | Frequency distribution of values across distinct buckets         |
| `mean`              | Average value—applies only to numeric columns                    |
| `standardDeviation` | Spread of values around the mean—applies only to numeric columns |

### Storage and quality

Metrics that describe the physical footprint and overall completeness of the column.

| Statistic           | What it tells you                                                            |
| ------------------- | ---------------------------------------------------------------------------- |
| `columnStoredBytes` | Bytes of storage consumed by the column                                      |
| `completeness`      | Ratio of non-null values to total rows, expressed as a value between 0 and 1 |

## How data types affect available statistics

Not all statistics apply to all data types. Numeric columns support the full set of 12 statistics, while non-numeric types like strings and booleans don't have `mean` or `standardDeviation`. Complex types like arrays and objects only support basic counts and storage metrics.

For the full mapping of which statistics are available for each data type, see the [type compatibility matrix](/reference/architecture/dataset-statistics#type-compatibility-matrix) in the reference.

## Related content

<CardGroup cols={2}>
  <Card title="Datasets" icon="table" href="/concepts/primitives/datasets">
    How datasets store, organize, and protect your data
  </Card>

  <Card title="Configuring Dataset Statistics" icon="gear" href="/guides/platform/configuring-dataset-statistics">
    Set up statistics through the platform UI
  </Card>

  <Card title="Dataset Statistics Reference" icon="chart-column" href="/reference/architecture/dataset-statistics">
    Full reference for each statistic and type compatibility matrix
  </Card>

  <Card title="Managing Datasets" icon="code" href="/guides/sdk/managing-datasets">
    Create and manage datasets with the SDK
  </Card>

  <Card title="Sample Data" icon="flask" href="/concepts/architecture/sample-data">
    How samples cross to the control plane
  </Card>
</CardGroup>