The uncertainty of an estimated quantity, such as the mean, rather than the spread of the data.The standard error is a measure of how precisely you know an estimated quantity, such as the mean of a dataset. It is not the spread of the individual data points (that's the standard deviation), but rather the uncertainty in the estimate itself. If you repeatedly took new samples and computed the mean each time, the standard error tells you how much those means would vary. It is the standard deviation of the sampling distribution of the statistic.
The concept was formalized by British statistician Ronald Fisher in the 1920s, who introduced the term 'standard error' to distinguish it from the standard deviation of the data itself.
The standard error is calculated by taking the standard deviation of the data and dividing by the square root of the sample size (SE = σ/√N). This formula arises because averaging many independent measurements reduces the random noise. The more data you have, the narrower the distribution of possible means becomes, so the standard error shrinks. It is the key number for constructing confidence intervals and performing hypothesis tests.
It is used in every scientific field that reports averages — from clinical trials (the error bars on drug efficacy) to economics (the uncertainty in unemployment rate estimates).
The standard error of the mean is always smaller than the standard deviation of the data, which is why averaging is such a powerful tool for reducing noise.





