Desviación estándar frente a error estándar: ¿cuál es la diferencia?

Twins from Different Universes

Standard Deviation and Standard Error are two statistical concepts that often cause confusion. Do they have the same interpretations or they are meant to represent something totally different? We’ll discuss more in this post.

What is Standard Deviation (SD)?

El desviación estándar mide el variabilidad (también conocido como el propagación) of data points around the personalizado in a given dataset. In other words, it tells us, on average, how far each data point is away from the mean.

Desviación estándar de población

In the real world, we’re interested in estimating a certain characteristic in a población. Standard deviation is an example of these characteristics.

Cuando tengas TODAS the data points from a population, you can compute the VERDADERO value of the population standard deviation using the following formula.

Desviación estándar de la muestra

Oftentimes, it is difficult to collect all the data points from the population due to time, financial, or technical limitations. For example, if we would like to compute the VERDADERO standard deviation of household income in Los Angeles, we would need to get income from all the households in Los Angeles, which is almost impossible to do.

Instead, we can collect random samples from the population and make inferences about the population standard deviation using Desviación estándar de la muestra. The formula for sample standard deviation is

Why use N-1 for sample standard deviation?

You will notice that we are using the sample mean (x̄) instead of the population mean (μ) for the sample standard deviation because we don’t know anything about the population mean. x̄ is a reasonable estimate for μ.

Therefore, any value X in the sample dataset would be closer to x̄ than to μ. The numerator in the sample standard deviation would get artificially smaller than it is supposed to be. As a result, the sample standard deviation would be subestimado.

To correct this parcialidad in the sample standard deviation, we would use “N-1” instead of “N” (alias, Corrección de Bessel) for sample standard deviation.

Using N-1 would make the sample standard deviation larger than otherwise using N. Therefore, we have a less biased estimate of the population standard deviation, giving us a conservative estimate of variability.

What is Standard Error (SE)?

Before we discuss the Standard Error, let’s first get familiar with the concepts of Sample Distribution y Distribución muestral.

Sample Distribution vs Sampling Distribution

El distribución de muestras es simplemente el distribución de datos of the sample which is randomly taken from the population.

For example, we ask 100 random people in Los Angeles what their incomes are. The sample distribution describes the REAL income distribution in these 100 people.

But what is Sampling Distribution?

El distribución muestral son los distribution of the sample statistic (e.g., the sample mean, sample variance, sample standard deviation, and sample proportion) over many samples drawn from the same population (i.e., muestreo repetido).

For example, we ask 100 random people in Los Angeles what their incomes are. Then compute the average income. We repeat this 1000 times, then we have 1000 different average incomes. The distribution of these 1000 average incomes is called the sampling distribution.

Por lo tanto, distribución de muestras is the distribution of the Data de muestra mientras distribución muestral is the distribution of the sample statistic.

El concepto es Error estándar is relevant to the sampling distribution, NOT the sample distribution.

El Error estándar is a metric that describes the variability of a statistic existentes sampling distribution.

How to interpret Standard Error (SE)?

The Standard Error measures how far the estadística de muestra (e.g., sample mean) is likely to be from the true population statistic (e.g., the population mean).

Why do we need Standard Error (SE)?

Typically you might want to construct intervalos de confianza when we try to make statistical inferences, and it is more informative to assign a probability to construct a confidence interval that contains the mean.

• If the underlying data are normally distributed, then the sampling distribution is also normally distributed. Then we can say we are 68% confident that the population mean lies within 1 standard error or 95% will be within 2 standard errors, etc.
• If the underlying data are NOT normally distributed, but the sample size is large enough, we can rely on Teorema del límite central (CLT) to say the sampling distribution is approximately normally distributed, then we can make similar statements about confidence intervals.

How to compute Standard Error (SE)?

We typically use the following formula to compute the standard error. I will discuss how to derive this formula in the next sections.

What are the examples of Standard Error?

Standard Error can be applied to various types of estadísticas. Algunos ejemplos populares son

• The Standard Error of the Sample Mean (aka, the standard error of the mean, SEM)
• The Standard Error of the Sample Proportion (aka, the standard error of the proportion, SEP)

What is the Standard Error of the Mean (SEM)?

The standard error of the mean (or simply standard error), indicates how different the muestra promedio is likely to be from the media poblacional.

Technically, the standard error of the mean is computed as the standard deviation of the sample mean.

Hypothetically, we can compute the standard error under repeated samples using the following steps:

1. Draw a new sample from the population.
2. Compute the sample mean of the drawn sample in Step 1
3. Repeat Steps 1 and 2 multiple times.
4. The standard error is obtained by computing the standard deviation of the previous steps’ sample means.

Gracias a Teorema del límite central (CLT), we don’t need to consider the Sampling Distribution under repeated samples. Instead, the sampling distribution of the sample means can be estimated from just ONE random sample.

The Central Limit Theorem states that the sample mean has an approximately normal distribution with a mean of μ y standard deviation (or standard error) of σ/√n.

How to derive the formula for SEM?

Por lo tanto,

In most cases, the standard deviation of the population data is unknown. We will estimate it using the standard deviation of the sample data (sample standard deviation).

Por lo tanto,

What is the Standard Error of the Proportion(SEP)?

The standard error of the proportion indicates how different the proporción de muestra is likely to be from the proporción de población.

The standard error of the proportion is computed as the standard deviation of the sample proportions.

You will notice that in each sample data, we only have data either 1 or 0. Each value follows a Bernouilli distribution. The computed sample proportions are no longer binary values. Instead, they could be any value between 0 and 1.

The Central Limit Theorem states that the sample proportion has an approximately normal distribution with a media de p y standard deviation (or standard error) of √P(1-P)/√n, where P is the population proportion.

How to derive the formula for SEP?

Similar to SEM,

Podemos estimar σ using the sample standard deviation √p(1-p) (i.e., the standard deviation of a Bernouilli distribution)

Conclusión:

Standard Deviation and Standard Error are similar concepts that both are used to measure variabilidad

Desviación Estándar indica como el sample data values are different from the mean in the distribución de muestras.

Error estándar indica como el sample data statistics are different from the population statistic in the distribución muestral.

Thank you for reading !!!

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