hi chiro

I finally figured it out. We need to first define effect size. In case of one sample hypothesis test on the population variance, the effect size is defined as the ratio of

true variance to the hypothesized variance. Then the power turns out to be (this is case for upper sided test)

\(\displaystyle P\left( \chi^2 > \frac{CV}{ES}\right) \)

where CV is the critical value

\(\displaystyle CV = \chi^2_{\alpha,n-1}\)

and ES is the effect size. We can use R for this. While looking for these answers, I also found a

free statistical software, G*power