1. Let's first understand what the standard error is. The standard error measures the variability or precision of a sample mean estimate of a population mean. 2. You need to calculate the standard error if you want to understand how much your sample mean is expected to vary from the true population mean. 3. The formula for the standard error of the mean is: $$SE = \frac{s}{\sqrt{n}}$$ where $s$ is the sample standard deviation and $n$ is the sample size. 4. Important rules: - If you have a large sample size, the standard error tends to be smaller, indicating more precise estimates. - The standard error is different from the standard deviation; the former relates to the sample mean's variability, the latter to individual data points. 5. In summary, calculate the standard error when you want to quantify the uncertainty in your sample mean estimate, especially for inferential statistics like confidence intervals or hypothesis testing. If your goal is descriptive statistics only, standard error may not be necessary.