Standard Error || Standard Error of the Mean || Meaning with Explanation || NTA UGC NET || Hindi/Eng

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In statistics, you’ll come across terms like “the standard error of the mean” or “the standard error of the median.” The SE tells you how far your sample statistic (like the sample mean) deviates from the actual population mean. The larger your sample size, the smaller the SE. In other words, the larger your sample size, the closer your sample mean is to the actual population mean. In statistics, a sample mean deviates from the actual mean of a population; this deviation is the standard error of the mean.
The term "standard error" is used to refer to the standard deviation of various sample statistics, such as the mean or median. For example, the "standard error of the mean" refers to the standard deviation of the distribution of sample means taken from a population. The smaller the standard error, the more representative the sample will be of the overall population. SE is used to estimate the efficiency, accuracy, and consistency of a sample. In other words, it measures how precisely a sampling distribution represents a population.
The standard error of the mean, or simply standard error, indicates how different the population mean is likely to be from a sample mean. It tells you how much the sample mean would vary if you were to repeat a study using new samples from within a single population.
The standard error of the mean (SE or SEM) is the most commonly reported type of standard error. But you can also find the standard error for other statistics, like medians or variance. The standard error is a common measure of sampling error-the difference between a population parameter and a sample statistic. You can decrease standard error by increasing sample size. Using a large, random sample is the best way to minimize sampling bias.
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Dr. Lalit M.Tiwari
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Uttarakhand
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How the value of standard error and how calculated standard error through Excel

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