Often times, I see researchers express their results either as Mean ± SD or Mean ± Standard error of mean.
Please throw more light on these, and state when to use either of them.
- The standard deviation (SD) measures the amount of variability or dispersion for a subject set of data from the mean, while the standard error of the mean (SE) measures how far the sample mean of the data is likely to be from the true population mean.
- SD is a descriptive statistic, whereas the SE is an inferential statistic.
- The SD is considered as one of the best measures of dispersion, which gauges the dispersion of values from the central value. On the other hand, the SE is mainly used to check the reliability and accuracy of the estimate and so, the smaller the error, the greater is its reliability and accuracy.
This video can be helpful for you: https://www.youtube.com/watch?v=A82brFpdr9g
Mean ± SD can be informative in case of normally distributed values only. One should be very careful with the results interpretation. The term Mean ± SD means nothing if there are large differences between mean, median and mode.
In graphs SD can be very long and it looks odd when SD is outside the mean, then SE is better.
SE = SD/ square root of n.
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