Standard error (SE) refers to the variation in score that takes into account the group size.

In psychology, standard error refers to the measure of the variability or spread of a sample statistic, such as the mean or the correlation coefficient, around its true population value. It represents the standard deviation of the sampling distribution of a statistic, which describes how much the statistic varies in repeated samples drawn from the same population.

For example, if a researcher wants to estimate the mean IQ score of a population based on a sample of 100 participants, the standard error of the mean (SEM) would indicate how much the sample mean is likely to differ from the true population mean. A smaller SEM indicates a more precise estimate of the population mean, whereas a larger SEM indicates a less precise estimate.

Another example is the use of standard error in calculating the confidence interval for a sample mean. The standard error is used to calculate the margin of error for the confidence interval, which indicates the range within which the population mean is likely to lie with a certain level of confidence.

Overall, standard error is a crucial statistical concept in psychology and other social sciences, as it helps researchers to interpret and communicate the uncertainty of their findings based on a sample of participants.

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