What Is Standard Error (SE)?

Standard error (SE) is a statistic that reveals how accurately sample data represents the whole population. It measures the accuracy with which a 澳洲幸运5开奖号码历史查询:sample distribution represents a population. In statistics, a sample🌟 mean deviates from the actual mean of a population; this deviation is the standard error of the mean.

The standard error is🧸 considered part of inferential statistics—or, the conclusions drawn from the study. It is inversely propo🌞rtional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value.

Understanding Standard Error

The term "standard error," or SE for short, is used to refer to the 澳洲幸运5开奖号码历史查询:standard deviation of various sample statistics, such as the mean or median.

When a population is sampled, the mean, or average, is generally calculated. The standard error describes t꧂he variation between the calculated mean of the population and one which is considered known, or accepted as accurate. This helps compensaౠte for any incidental inaccuracies related to the gathering of the sample.

The "standard error of the mean" refers to the standard deviation of the distribution of sample means taken from a population. The relationship between the standard error and the standard deviation is such that, for a given sample size, the standard error equals the standard deviation divided by the square root of the sample size.

The deviation of the standard error is expressed as a number. Sometimes, the deviation is necessary or desired to be shown as a percentage. When shown as a percentage it is known as the 澳洲幸运5开奖号码历史查询:relative standard error.

The smaller the standard error, the more representative the sample will be of the overall population. And the more data points involved in the calculations of the mean, the smaller the standard error tends to be. In cases where the standard error is large, the data may have some notable irregularities.

In cases where multiple samples are collected, the mean of eachꦫ sample may vary slightly from the others, creating a spread among the variables. This spread is most often measured as the standard error, accounting for the differences between the means across the 😼datasets.

Formula and Calculation of Standard Error

Used in algorithmic trading, t♔he standard error of an estimate can be calculated as the standard deviation divided by the square root of the sample size:

$\begin{aligned}&\text{SE} = \frac{\sigma}{\surd n}\\&\textbf{where:}\\&\sigma=\text{The population standard deviation}\\&\surd n = \text{The square root of the sample size}\end{aligned}$​SE=√nσ​where:σ=The population standard deviation√n=The&🍬nbsp;squareꦑ root of the sample size​

If the population standard deviation is not known, you can substitute the sample standard deviation, s, in the numerator to approx♛imate the standard err🔯or.

Standard Error vs. Standard Deviation

The standaꦇrd deviation is a representation of the spread of each of the data points. It is used to help determine the validity of the data based on the number of data points displayed at each level of standard deviation.

Standard errors function more as a way to determine the accuracy of the sample or the accuracy of multiple samples by analyzing deviation withiꦉn the means.

The standard error normalizes the standard deviation relative to the sample size used in an a🔜nalysis. Standard deviation measures the amount of variance or dispersion of the data spread around the mean. The standard error can be thought of as the dispersion of the sample mean estimations around the true population mean.

Standard Error and Confidence Intervals

When statisticians or researchers estimate a population parameter, they rarely know the exact value. Instead, they use sample data to make an educated guess. As discussed above, this is the confidence interval. This range gives a sense of how much uncertainty surrounds the estimate.

Understanding the role of standard error in confidence intervals can help interpret study results. For example, if two studies estimate the same population mean but have very different confidence intervals, it's often because of differences in standard error. A narrow interval implies greater confidence in the estimate’s precision, while a wider one suggests the need for more data or better sampling methods. In practical terms, confidence intervals—powered by standard error—help researchers and decision-makers gauge the reliability of the numbers they rely on.

Standard Error and Hypothesis Testing

Standard error also impacts hypothesis testing, especially when comparing sample statistics to population parameters. In tests like the z-test or t-test, you use the standard error to measure how far the sample result is from what we would expect if the nul🔯l hypothesis were true. It helps determine whether the observed effect is likely due to chance or if it's statistically significant. Without standard error, we wouldn't have a way to judge the reliability of the sample estimཧate.

In both z-tests and t-tests, the test statistic is calculated by comparing the sample statistic, the hypothesized value, and the standard ꦉerror. This ratio tells us how many standard errors the sample statistic is away from the value assumed under the null hypothesis. A large test statistic suggests the sample result is far from the expected value, which could indicate that the null hypothesis should be rejected. If the result is close—within about two standard errors, for example—it’s likely not significant.

This test statistic is then used to find a p-value, which tells us the probability of observing a result as extreme as the one in the sample, assuming the null hypothesis is true. The smaller the standard error, the larger the test statistic will be. That’s why standard error di൲rectly af📖fects whether a result is statistically significant.

You can also conceptualize this by thinking of a bell curve where the center represents the null hypothesis. 🎉If the standard error is large, the curve is wider and flatter, meaning observed results must be farther from the center to be considered unusual. If the standard error is small, the curve is tighter, and even small differences from the center appear extreme.

Downsides to Using Standard Error

On🍃e of the biggest downsides to using standard error is that standard error assumes a random and representative sample. If the sample is biased or poorly collected, the calculated standard error may underestimate or misrepresent the true uncertainty. This can lead to misleading confidence intervals or inaccurate hypothesis tests.

Another limitation is that the standard error becomes less reliable with small sample sizes. In small samples, the estimate of variability may not reflect the true population variance, which affect🍒s the accuracy of the standard error. This is why t-tests, which adjust for small sample sizes using the t-distribution, are often preferred over z-tests when dealing with limited data.

Standard error also assumes that the data being analyzed follows a certain distribution, typically a normal distribution. If the underlying data is highly skewed, contains outliers𝐆, or doesn't meet the assumptions of the central limit theorem, then the standard error might not accurately show the true variability of the estimate.

Example of Standard Error

Say that an analyst has looked at a random sample of 50 companies in the S&P 500 to understand the association between a stock'💯s P/E ratio and subsequent 12-month performance in ♉the market.

Assume that the resulting estimate is -0.20, indicating that for every 1.0 point in the 澳洲幸运5开奖号码历史查询:P/E ratio, stocks return 0.2% poorer relative performance. In the sample of 50, the standard deviation was found t🌼o be 1.0.

The standard error is thus:

$\begin{aligned}&\text{SE} = \frac{1.0}{\surd50} = \frac{1}{7.07} = 0.141\end{aligned}$​SE=√501.0​=7.071​=0.141​

Therefore, we would report the estimate as -0.20% ± 0.14, giving us a confidence interval of (-0.34 - -0.06). 🍸The true mean value of the association of the P/E on return🍸s of the S&P 500 would therefore fall within that range with a high degree of probability.

Say now that we increase the sample of stocks to 100 and find that the estimate changes slightly frꦬom -0.20 to -0.25, and the standard deviation falls to 0.90. The new standard errorܫ would thus be:

$\begin{aligned}&\text{SE} = \frac{0.90}{\surd100} = \frac{0.90}{10} = 0.09.\end{aligned}$​SE=√1000.90​=100.90​=0.09.​

The resulting conf𓃲idence inter🧜val becomes -0.25 ± 0.09 = (-0.34 - -0.16), which is a tighter range of values.

How Will I Use This in Real Life?

Generally, if you're analyzing data, you'll need to be aware of the standard error because it can tell you how much variation is in the sampled data. Regarding investing and trading, it can be used to evaluate how likely and how much an asset's future returns are to fluctuate and the reliability of historical averages.

For instance, if your analysis results in a standard error of -0.25 ± 0.09 = (-0.34 - -0.16), you can make an educated guess that your investment's future returns should fall within this range (as long as market conditions remain the same).

The Bottom Line

The standard error (SE) measures the dispersion of estimated values obtained from a sample around the true value to be found in the poꦅpulation.

Statistical analysis and inference often involꦗve drawing samples and running statistical tests to determine associations and correlations between variables. The standard error thus tells us with what degree of confidence we can expect the estimated value to approximate the population value.