# `SKEWNESS_SAMP`

**Applies to:** ✅ Data 360 SQL ✅ Tableau Hyper API

Computes the sample skewness of input values, with a bias correction for finite samples. Skewness measures the asymmetry of a distribution: positive values indicate a longer right tail, negative values indicate a longer left tail, and zero indicates a symmetric distribution.

## Syntax

```sql
skewness_samp(<expression>)
```

## Arguments

### Required

- `<expression>`: An expression of any numerical type.

## Returns

Returns a `double precision` value.

Returns NULL when fewer than three non-null input values are provided or when all non-null values are identical.

## Considerations

- Use `SKEWNESS_SAMP` when the data is a sample drawn from a larger population, the typical analytics case. Use [`SKEWNESS_POP`](/docs/data/data-cloud-query-guide/references/dc-sql-reference/skewness-pop.md) when the data is the complete population of interest.
- Uses the adjusted Fisher–Pearson coefficient of skewness, with finite-sample bias correction.
- Converges with `SKEWNESS_POP` for large row counts.
- Ignores NULL inputs. Any NaN or infinite input propagates to a NaN result.

## Examples

### Calculate Sample Skewness

Estimate the skewness of service latency from a sampled slice of requests.

```sql
SELECT skewness_samp(latency_ms) AS latency_skew
FROM request_log_sample;
```

### Skewness by Group

Compare the asymmetry of claim amounts across policy lines.

```sql
SELECT policy_line, skewness_samp(claim_amount) AS claim_skew
FROM claims
GROUP BY policy_line;
```

## Related Documentation

- [SKEWNESS_POP](/docs/data/data-cloud-query-guide/references/dc-sql-reference/skewness-pop.md)
- [KURTOSIS](/docs/data/data-cloud-query-guide/references/dc-sql-reference/kurtosis.md)
- [STDDEV](/docs/data/data-cloud-query-guide/references/dc-sql-reference/stddev.md)
- [Aggregate Functions](/docs/data/data-cloud-query-guide/references/dc-sql-reference/aggregate.md)