You need an idea of how large the standard deviation of your measurement might be.

Then you can use a formula which gives you the standard error for your sample mean,

depending on sample size and standard deviation. The formula is

*standard error = standard deviation/square root of sample size.*
For example, if resilience has a standard deviation of 30, then the standard error would be

3

*0/square root of 100 = 30/10 = 3 .*
Now, with your standard error you can compute a conficence interval for your

sample mean, which gives you an idea how precise your estimates will be.

For the limits of a 95% confidence interval around your mean, you multiply the

standard error with 1.96. This would result in a confidence interval of +/- 5.9 points

around the mean. For a 90% interval, multiply with 1.65.

You can play around with this using e.g.

https://www.socscistatistics.com/confidenceinterval/default3.aspx
Mind that the assumed standard deviation is crucial.

Which precision you want to achieve by increasing your sample size, is up to your

judgement, or up to the requirements of your customer.

Just my 2pence

Karabiner