Square standard error

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The standard error (SE)^[1] of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution^[2] or an estimate of that standard deviation. If the statistic is the sample mean, it is called the standard error of the mean (SEM).^[1]

The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance. Mathematically, the variance of the sampling mean distribution obtained is equal to the variance of the population divided by the sample size. This is because as the sample size increases, sample means cluster more closely around the population mean.

Therefore, the relationship between the standard error of the mean and the standard deviation is such that, for a given sample size, the standard error of the mean equals the standard deviation divided by the square root of the sample size.^[1] In other words, the standard error of the mean is a measure of the dispersion of sample means around the population mean.

In regression analysis, the term «standard error» refers either to the square root of the reduced chi-squared statistic or the standard error for a particular regression coefficient (as used in, say, confidence intervals).

Standard error of the sample mean[edit]

Exact value[edit]

Suppose a statistically independent sample of observations is taken from a statistical population with a standard deviation of . The mean value calculated from the sample, , will have an associated standard error on the mean, , given by:^[1]

.

Practically this tells us that when trying to estimate the value of a population mean, due to the factor , reducing the error on the estimate by a factor of two requires acquiring four times as many observations in the sample; reducing it by a factor of ten requires a hundred times as many observations.

Estimate[edit]

The standard deviation of the population being sampled is seldom known. Therefore, the standard error of the mean is usually estimated by replacing with the sample standard deviation instead:

.

As this is only an estimator for the true «standard error», it is common to see other notations here such as:

  or alternately   .

A common source of confusion occurs when failing to distinguish clearly between:

Accuracy of the estimator[edit]

When the sample size is small, using the standard deviation of the sample instead of the true standard deviation of the population will tend to systematically underestimate the population standard deviation, and therefore also the standard error. With n = 2, the underestimate is about 25%, but for n = 6, the underestimate is only 5%. Gurland and Tripathi (1971) provide a correction and equation for this effect.^[3] Sokal and Rohlf (1981) give an equation of the correction factor for small samples of n < 20.^[4] See unbiased estimation of standard deviation for further discussion.

Derivation[edit]

The standard error on the mean may be derived from the variance of a sum of independent random variables,^[5] given the definition of variance and some simple properties thereof. If are independent samples from a population with mean and standard deviation , then we can define the total

which due to the Bienaymé formula, will have variance

where we’ve approximated the standard deviations, i.e., the uncertainties, of the measurements themselves with the best value for the standard deviation of the population. The mean of these measurements is simply given by

.

The variance of the mean is then

The standard error is, by definition, the standard deviation of which is simply the square root of the variance:

.

For correlated random variables the sample variance needs to be computed according to the Markov chain central limit theorem.

Independent and identically distributed random variables with random sample size[edit]

There are cases when a sample is taken without knowing, in advance, how many observations will be acceptable according to some criterion. In such cases, the sample size is a random variable whose variation adds to the variation of such that,

^[6]

If has a Poisson distribution, then with estimator . Hence the estimator of becomes , leading the following formula for standard error:

(since the standard deviation is the square root of the variance)

Student approximation when σ value is unknown[edit]

In many practical applications, the true value of σ is unknown. As a result, we need to use a distribution that takes into account that spread of possible σ’s.
When the true underlying distribution is known to be Gaussian, although with unknown σ, then the resulting estimated distribution follows the Student t-distribution. The standard error is the standard deviation of the Student t-distribution. T-distributions are slightly different from Gaussian, and vary depending on the size of the sample. Small samples are somewhat more likely to underestimate the population standard deviation and have a mean that differs from the true population mean, and the Student t-distribution accounts for the probability of these events with somewhat heavier tails compared to a Gaussian. To estimate the standard error of a Student t-distribution it is sufficient to use the sample standard deviation «s» instead of σ, and we could use this value to calculate confidence intervals.

Note: The Student’s probability distribution is approximated well by the Gaussian distribution when the sample size is over 100. For such samples one can use the latter distribution, which is much simpler.

Assumptions and usage[edit]

An example of how is used is to make confidence intervals of the unknown population mean. If the sampling distribution is normally distributed, the sample mean, the standard error, and the quantiles of the normal distribution can be used to calculate confidence intervals for the true population mean. The following expressions can be used to calculate the upper and lower 95% confidence limits, where is equal to the sample mean, is equal to the standard error for the sample mean, and 1.96 is the approximate value of the 97.5 percentile point of the normal distribution:

Upper 95% limit and

Lower 95% limit

In particular, the standard error of a sample statistic (such as sample mean) is the actual or estimated standard deviation of the sample mean in the process by which it was generated. In other words, it is the actual or estimated standard deviation of the sampling distribution of the sample statistic. The notation for standard error can be any one of SE, SEM (for standard error of measurement or mean), or S_E.

Standard errors provide simple measures of uncertainty in a value and are often used because:

• in many cases, if the standard error of several individual quantities is known then the standard error of some function of the quantities can be easily calculated;
• when the probability distribution of the value is known, it can be used to calculate an exact confidence interval;
• when the probability distribution is unknown, Chebyshev’s or the Vysochanskiï–Petunin inequalities can be used to calculate a conservative confidence interval; and
• as the sample size tends to infinity the central limit theorem guarantees that the sampling distribution of the mean is asymptotically normal.

Standard error of mean versus standard deviation[edit]

In scientific and technical literature, experimental data are often summarized either using the mean and standard deviation of the sample data or the mean with the standard error. This often leads to confusion about their interchangeability. However, the mean and standard deviation are descriptive statistics, whereas the standard error of the mean is descriptive of the random sampling process. The standard deviation of the sample data is a description of the variation in measurements, while the standard error of the mean is a probabilistic statement about how the sample size will provide a better bound on estimates of the population mean, in light of the central limit theorem.^[7]

Put simply, the standard error of the sample mean is an estimate of how far the sample mean is likely to be from the population mean, whereas the standard deviation of the sample is the degree to which individuals within the sample differ from the sample mean.^[8] If the population standard deviation is finite, the standard error of the mean of the sample will tend to zero with increasing sample size, because the estimate of the population mean will improve, while the standard deviation of the sample will tend to approximate the population standard deviation as the sample size increases.

Extensions[edit]

Finite population correction (FPC)[edit]

The formula given above for the standard error assumes that the population is infinite. Nonetheless, it is often used for finite populations when people are interested in measuring the process that created the existing finite population (this is called an analytic study). Though the above formula is not exactly correct when the population is finite, the difference between the finite- and infinite-population versions will be small when sampling fraction is small (e.g. a small proportion of a finite population is studied). In this case people often do not correct for the finite population, essentially treating it as an «approximately infinite» population.

If one is interested in measuring an existing finite population that will not change over time, then it is necessary to adjust for the population size (called an enumerative study). When the sampling fraction (often termed f) is large (approximately at 5% or more) in an enumerative study, the estimate of the standard error must be corrected by multiplying by a »finite population correction» (a.k.a.: FPC):^[9]
[10]

which, for large N:

to account for the added precision gained by sampling close to a larger percentage of the population. The effect of the FPC is that the error becomes zero when the sample size n is equal to the population size N.

This happens in survey methodology when sampling without replacement. If sampling with replacement, then FPC does not come into play.

Correction for correlation in the sample[edit]

If values of the measured quantity A are not statistically independent but have been obtained from known locations in parameter space x, an unbiased estimate of the true standard error of the mean (actually a correction on the standard deviation part) may be obtained by multiplying the calculated standard error of the sample by the factor f:

where the sample bias coefficient ρ is the widely used Prais–Winsten estimate of the autocorrelation-coefficient (a quantity between −1 and +1) for all sample point pairs. This approximate formula is for moderate to large sample sizes; the reference gives the exact formulas for any sample size, and can be applied to heavily autocorrelated time series like Wall Street stock quotes. Moreover, this formula works for positive and negative ρ alike.^[11] See also unbiased estimation of standard deviation for more discussion.

See also[edit]

• Illustration of the central limit theorem
• Margin of error
• Probable error
• Standard error of the weighted mean
• Sample mean and sample covariance
• Standard error of the median
• Variance

References[edit]

Standard error of the mean measures how spread out the means of the sample can be from the actual population mean. Standard error allows you to build a relationship between a sample statistic (computed from a smaller sample of the population) and the population’s actual parameter.

Standard Error – A practical guide with examples. Photo by Sergio.

What is Standard Error?

The sample error serves as a means to understand the actual population parameter (like population mean) without actually estimating it.

Consider the following scenario:

A researcher ‘X’ is collecting data from a large population of voters. For practical reasons he can’t reach out to each and every voter. So, only a small randomized sample (of voters) is selected for data collection.

Once the data for the sample is collected, you calculate the mean (or any statistic) of that sample. But then, this mean you just computed is only the sample mean. It cannot be considered the entire population’s mean. You can however expect it to be somewhere close to population’s mean.

So how can you know the actual population mean?

While its not possible to compute the exactly value, you can use standard error to estimate how far the sample mean may spread from the actual population mean.

To be more precise,

The Standard Error of the Mean describes how far a sample mean may vary from the population mean.

In this post, you will understand clearly:

1. What Standard Error Tells Us?
2. What is the Sample Error Formula?
3. How to calculate Standard Error?
4. How to use standard error to compute confidence interval?
5. Example Problem and solution

How to understand Standard Error?

Let’s first clearly understand the intuition behind and the need for standard error.

Now, let’s suppose you are working in agriculture domain and you want to know the annual yield of a particular variety of coconut trees. While the entire population of coconut trees has a certain mean (and standard deviation) of annual yield, it is not practical to take measurements of each and every tree out there.

So, what do you do?

To estimate this you collect samples of coconut yield (number of nuts per tree per year) from different trees. And to keep your findings unbiased, you collect samples across different places.

Let’s say, you collected data from approx ~5 trees per sample from different places and the numbers are shown below.

# Annual yield of coconut
sample1 = [400, 420, 470, 510, 590]

sample2 = [430, 500, 570, 620, 710, 800, 900]

sample3 = [360, 410, 490, 550, 640]

In above data, the variables sample1, sample2 and sample3 contain the samples of annual yield values collected, where each number represents the yield of one individual tree.

Observe that the yield varies not just across the trees, but also across the different samples.

Although we compute means of the samples, we are actually not interested in the means of the sample, but the overall mean annual yield of coconut of this variety.

Now, you may ask: ‘Why can’t we just put the values from all these samples in one bucket and simply compute the mean and standard deviation and consider that as the population’s parameter?‘

Well, the problem is, if you do that practically what happens is, as you receive few more samples, the real population’s parameter begins to come out which is likely to be (slightly) different from the parameter you computed earlier.

Below is a code demo.

from statistics import mean, stdev

# Overall mean from the first two samples
sample1 = [400, 420, 470, 510, 590]
sample2 = [430, 500, 570, 620, 710, 800, 900]
print("Mean of first two samples: ", mean(sample1 + sample2))

# Overall mean after introducing 3rd sample
sample3 = [360, 410, 490, 550, 640]
print("Mean after including 3rd sample: ", mean(sample1 + sample2 + sample3))

Output:

Mean of first two samples:  576.6666666666666
Mean after including 3rd sample:  551.1764705882352

As you add more and more samples, the computed parameters keep changing.

So how to tackle this?

If you notice, each sample has its own mean that varies between a particular range. This mean (of the sample) has its own standard deviation. This measure of standard deviation of the mean is called the standard error of the mean.

Its important to note, it is different from the standard deviation of the data. The difference is, while standard deviation tells you how the overall data is distributed around the mean, the standard error tells you how the mean itself is distributed.

This way, it can be used to generalize the sample mean so it can be used as an estimate of the whole population.

In fact, standard error can be generalized to any statistic like standard deviation, median etc. For example, if you compute the standard deviation of the standard deviations (of the samples), it is called, standard error of the standard deviation. Feels like a tongue twister. But most commonly, when someone mention ‘Standard error’ it typically refers to the ‘Standard error of of the mean’.

What is the Formula?

To calculate standard error, you simply divide the standard deviation of a given sample by the square root of the total number of items in the sample.

$$SE_{bar{x}} = frac{sigma}{sqrt{n}}$$

where, $SE_{bar{x}}$ is the standard error of the mean, $sigma$ is the standard deviation of the sample and n is the number of items in sample.

Do not confuse this with standard deviation. Because standard error of the sample statistic (like mean) is typically much smaller than the population standard deviation.

Notice few things here:

1. The Standard error depends on the number of items in the sample. As you increase the number of items in the sample, lower will be the standard error and more certain you will be about the estimates.
2. It uses statistics (standard deviation and number of items) computed from the sample itself, and not of the population. That is, you don’t need to know the population parameters beforehand to compute standard error. This makes it pretty convenient.
3. Standard error can also be used as an estimate of how representative a given sample is of a population. The smaller the value, more representative is the sample of the whole population.

Below is a computation for the standard error of the mean:

# Compute Standard Error
sample1 = [400, 420, 470, 510, 590]
se = stdev(sample1)/len(sample1)
print('Standard Error: ', round(se, 2))

Standard Error:  15.19

How to calculate standard error?

Problem Statement

A school aptitude test for 15 year old students studying in a particular territory’s curriculum, is designed to have a mean score of 80 units and a standard deviation of 10 units. A sample of 15 answer papers has a mean score of 85. Can we assume that these 15 scores come from the designated population?

Solution

Our task is to determine if this sample comes from the above mentioned population.

How to solve this?

We approach this problem by computing the standard error of the sample means and use it to compute the confidence interval between which the sample means are expected to fall.

If the given sample mean falls inside this interval, then its safe to assume that the sample comes from the given population.

Time to get into the math.

Using standard error to compute confidence interval

Standard error is often used to compute confidence intervals

We know, n = 15, x_bar = 85, σ = 10

$$SE_bar{x} = frac{sigma}{sqrt{n}} = frac{10}{sqrt{15}} = 2.581$$

From a property of normal distribution, we can say with 95% confidence level that the sample means are expected to lie within a confidence interval of plus or minus two standard errors of the sample statistic from the population parameter.

But where did ‘normal distribution’ come from? You may wonder how we can directly assume a normal distribution is followed in this case. Or rather shouldn’t we test if the sample follows a normal distribution first before computing the confidence intervals.

Well, that’s NOT required. Because, the Central Limit Theorem tells us that even if a population is not normally distributed, a collection of sample means from that population will infact follow a normal distribution. So, its a valid assumption.

Back to the problem, let’s compute the confidence intervals for 95% Confidence Level.

• Lower Limit : 80 - (2*2.581) = 74.838
• Upper Limit : 80 + (2*2.581) = 85.162

So, 95% of our 15 item sample means are expected to fall between 74.838 and 85.162.

Since the sample mean of 85.0 lies within the computed range, there is no reason to believe that the sample does not belong to the population.

Conclusion

Standard error is a commonly used term that we sometimes ignore to fully understand its significance. I hope the concept is clear and you can now relate how you can use standard error in appropriate situations.

Next topic: Confidence Interval