I am afraid that without knowing either the individual observations or the sum-of-squares (sum of the squared discrepancies around the mean), there is no way of identifying the sample standard deviation. Let me tell you an example. Consider the two datasets (1, 3, 5, 7 and 9) and (3, 4, 5, 6 and 7). Both of them form a sample of n=5 units and possess the same sample mean of 5. Their standard deviation, however, is not the sample. Unfortunately, the sample mean and the sample size are not sufficient to identify the corresponding standard deviation.
You cannot directly calculate the standard deviation from just the mean and the sample size. The standard deviation is a measure of the spread or dispersion of data points around the mean, and it requires the individual data values to be available to compute it accurately.
However, if you have access to the entire dataset, you can use the following steps to calculate the standard deviation from the mean and the dataset:
Calculate Deviations: For each data point, subtract the mean from that data point. This gives you a set of deviations from the mean.
Square Deviations: Square each of the deviations you calculated in the previous step.
Calculate Mean of Squared Deviations: Calculate the mean of the squared deviations.
Calculate Square Root: Take the square root of the mean of squared deviations calculated in the previous step. This will give you the standard deviation.