IntermedioTutoriales·15 min

Calcular el Desvío Estándar en Excel y Python

Tutoriales paso a paso para calcular el desvío estándar en Excel (STDEV.S, STDEV.P) y Python (numpy, pandas, statistics). Con ejemplos de código.

Excel: Descripción general

Microsoft Excel proporciona funciones integradas para calcular tanto el desvío estándar muestral como el poblacional. Estas funciones están disponibles en todas las versiones modernas de Excel.

Funciones de Excel

Función	Tipo	Descripción
`STDEV.S()`	Muestral	Desvío estándar muestral (divide por n-1)
`STDEV.P()`	Poblacional	Desvío estándar poblacional (divide por N)
`STDEV()`	Muestral	Función heredada, igual que STDEV.S
`STDEVP()`	Poblacional	Función heredada, igual que STDEV.P

Ejemplos en Excel

Excel Formulas

// Data in cells A1:A10
=STDEV.S(A1:A10)     // Sample SD
=STDEV.P(A1:A10)     // Population SD

// For specific values
=STDEV.S(4, 8, 6, 5, 3)    // Returns 1.924

// Ignoring text and logical values
=STDEV.S(A1:A10)    // Ignores text
=STDEVA(A1:A10)     // Includes text as 0

Consejo profesional

Usá STDEV.S para la mayoría de los análisis del mundo real. Solo usá STDEV.P cuando estés seguro de que tenés la población completa.

Python: Descripción general

Python ofrece múltiples formas de calcular el desvío estándar. Las bibliotecas más comunes son NumPy, Pandas y el módulo integrado statistics.

Usando NumPy

Python (NumPy)

import numpy as np

data = [4, 8, 6, 5, 3]

# Population standard deviation (default)
pop_sd = np.std(data)
print(f"Population SD: {pop_sd}")  # 1.720

# Sample standard deviation
sample_sd = np.std(data, ddof=1)
print(f"Sample SD: {sample_sd}")  # 1.924

¿Qué es ddof?

ddof significa “Delta Degrees of Freedom” (Delta de grados de libertad). Configurar ddof=1 le indica a NumPy que divida por (n-1) para el DE muestral. El valor predeterminado ddof=0 da el DE poblacional.

Usando Pandas

Python (Pandas)

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'scores': [85, 90, 78, 92, 88]})

# Sample SD (default in pandas)
sample_sd = df['scores'].std()
print(f"Sample SD: {sample_sd}")

# Population SD
pop_sd = df['scores'].std(ddof=0)
print(f"Population SD: {pop_sd}")

# Multiple columns at once
df.std()  # Returns SD for all numeric columns

Comparación rápida

Herramienta	DE muestral	DE poblacional
Excel	`STDEV.S()`	`STDEV.P()`
NumPy	`np.std(data, ddof=1)`	`np.std(data)`
Pandas	`df.std()`	`df.std(ddof=0)`
Python statistics	`stdev()`	`pstdev()`

How to Read This Article

A statistics tutorial is a practical interpretation guide, not just a formula dump. It refers to the assumptions, notation, and reporting language that analysts need when they explain a result to a teacher, manager, client, or reviewer. The article body covers the specific topic, while the sections below create a common interpretation frame that readers can reuse across related metrics.

Reading goal	What to focus on	Common mistake
Definition	What the metric is and what quantity it summarizes	Treating the formula as self-explanatory
Formula choice	Sample versus population assumptions and notation	Using n when n-1 is required or vice versa
Interpretation	Whether the result indicates concentration, spread, or risk	Calling a large value good or bad without context

Frequently Asked Questions

How should I interpret a high standard deviation?

A high standard deviation means the observations are spread farther from the mean on average. Whether that spread is acceptable depends on the context: wide dispersion might signal risk in finance, instability in manufacturing, or genuine natural variation in scientific data.

Why do some articles mention n while others mention n-1?

The denominator reflects the difference between population and sample formulas. Population variance and population standard deviation use N because the full dataset is known. Sample variance and sample standard deviation often use n-1 because Bessel’s correction reduces bias when estimating population spread from a sample.

What is a statistical interpretation guide?

A statistical interpretation guide is a page that moves beyond arithmetic and explains meaning. It tells you what a metric is, when the formula applies, and how to describe the result in plain English without overstating certainty.

Can I cite this article in a report?

You should cite the underlying authoritative reference for formal work whenever possible. This page is best used as an explanatory bridge that helps you understand the concept before quoting the original standard or handbook.

Why include direct citations on every article page?

Direct citations give readers a route to verify the definition, notation, and assumptions. That improves trust and reduces the chance that a simplified explanation is mistaken for the entire technical standard.

Authoritative References

These sources define the concepts referenced most often across our articles. Bessel's correction is a sample adjustment, variance is a squared measure of spread, and standard deviation is the square root of variance expressed in the same units as the data.