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

# Overview

> What Statistics metrics are in Prizm — compute numeric summaries of column values like mean, median, quantiles, and distribution shape.

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## What is a Statistics metric?

Statistics metrics compute **numeric summaries of column values** — central tendency, spread, quantile boundaries, distribution shape, and aggregate totals. They provide the statistical fingerprint of a column and detect subtle data drift that isn't visible in row counts or null rates.

**Type:** Configurable — Statistics metrics are part of the Structural (Advanced) profile. They are enabled per column through the Configure Structural Metrics panel.

Statistics metrics answer the question: *What are the statistical properties of this column, and have they changed?*

### Metrics

| Metric              | Level     | Description                                                                                                                     |
| ------------------- | --------- | ------------------------------------------------------------------------------------------------------------------------------- |
| Mean                | Attribute | Arithmetic average of all non-null values                                                                                       |
| Median              | Attribute | Middle value when all values are sorted — more robust than Mean for skewed distributions                                        |
| Mode                | Attribute | Most frequently occurring value                                                                                                 |
| Sum                 | Attribute | Total of all numeric values — useful for financial columns where the aggregate should match an expected total                   |
| Q1                  | Attribute | 25th percentile — the value below which 25% of observations fall                                                                |
| Q3                  | Attribute | 75th percentile — the value below which 75% of observations fall                                                                |
| Range               | Attribute | Difference between the maximum and minimum values                                                                               |
| Standard Deviation  | Attribute | Measure of how spread out values are around the mean                                                                            |
| Variance            | Attribute | Square of Standard Deviation — tracks distribution spread over time                                                             |
| Skewness            | Attribute | Measures asymmetry of the distribution — positive skew means a long right tail; negative skew means a long left tail            |
| Kurtosis            | Attribute | Measures tail heaviness relative to a normal distribution — high kurtosis means more extreme values                             |
| The Margin of Error | Attribute | Statistical margin of error for the column's mean estimate — indicates confidence in the Mean metric at the current sample size |

### How statistics monitoring works

Statistics are computed on each profiling run across all enabled numeric and date columns. Prizm baselines each statistic independently and applies anomaly detection to flag meaningful drift. Because these metrics describe the population rather than individual records, they are particularly effective at detecting upstream changes that affect many records simultaneously — loader bugs, ETL logic changes, or population shifts in source systems.

### Example use cases

**Financial reconciliation:** The `payment_amount` column in a payments table should have a relatively stable Mean and Sum across daily loads. A sudden Mean spike on a Tuesday with no corresponding business event triggers an investigation that uncovers a misconfigured FX conversion multiplier applied to a subset of records.

**Detecting population shift:** A machine learning feature column `session_duration_seconds` has a long-established right-skewed distribution (Skewness ≈ 2.3). After a mobile app update changes how sessions are defined, Skewness drops to 0.4. Prizm alerts on the shape change — flagging that the feature distribution has fundamentally changed before it causes model degradation.

<Tip>
  Use Q1 and Q3 alongside Mean to distinguish between mean drift and distribution shape changes. A Mean that stays constant while Q1 and Q3 diverge indicates the distribution is widening — which Mean alone would not reveal.
</Tip>

<Card title="Configure" icon="sliders" href="/architecture/metrics/statistics/configure">
  Enable Statistics metrics per column, set thresholds, and configure alerts.
</Card>