This has a clear meaning for data that are normally distributed (i.e. 'Gaussian'). Data that can only take positive values cannot be Gaussian, but for some part of the curve it may look 'similar' to Gaussian.
If data are normally distributed, then a bit less than two thirds is in between the range you propose and one third is outside, so still quite a lot.
For distributions with an unknown shape, one can use 'percentiles'. The 95-percentile is used a lot, because in a normal distribution this also represents 2-sigma. There is only 5% probability that the quantity is not between the two limits.
This is not computed as easily as mean and std.dev. (one pass through the data) but Excel may have functions for this (?)
If data are normally distributed, then a bit less than two thirds is in between the range you propose and one third is outside, so still quite a lot.
For distributions with an unknown shape, one can use 'percentiles'. The 95-percentile is used a lot, because in a normal distribution this also represents 2-sigma. There is only 5% probability that the quantity is not between the two limits.
This is not computed as easily as mean and std.dev. (one pass through the data) but Excel may have functions for this (?)
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