---
name: detect_outliers
kind: function
lang: py
domain: datascience
version: "1.0.0"
purity: pure
signature: "def detect_outliers(data: list, threshold: float) -> list"
description: "Detecta outliers por z-score. Retorna lista de bools, True donde |z-score| > threshold."
tags: [statistics, outliers, python, pendiente-usar, validator]
uses_functions: []
uses_types: []
returns: []
returns_optional: false
error_type: ""
imports: [math]
params:
  - name: data
    desc: "lista de valores numericos para detectar outliers"
  - name: threshold
    desc: "umbral de z-score absoluto (tipico: 2.0 para 95% confianza, 3.0 para 99%). Mayor = menos sensible."
output: "lista de booleanos paralela a data, True donde |z-score| > threshold"
tested: false
tests: []
test_file_path: ""
file_path: "python/functions/datascience/datascience.py"
---

## Ejemplo

```python
detect_outliers([1, 2, 3, 100, 2, 3], 2.0)
# [False, False, False, True, False, False]
```

## Notas

Usa z-score poblacional. Threshold tipico: 2.0 o 3.0. Si la desviacion es cero, no hay outliers.
