Confused about degrees of freedom Everything I've read about degrees of freedom says more is better - it is an indication of a model's flexibility.
I'm now running a power analysis in R and I've noticed that lower degrees of freedom give higher power.
Example 1 (DF = N - 1):
library(pwr)

pwr.chisq.test(w = 0.7, N = 30, df = 29, sig.level = 0.05)

     Chi squared power calculation 

              w = 0.7
              N = 30
             df = 29
      sig.level = 0.05
          power = 0.5115032

NOTE: N is the number of observations

Example 2 (DF = 1):
pwr.chisq.test(w = 0.7, N = 30, df = 1, sig.level = 0.05) 

     Chi squared power calculation 

              w = 0.7
              N = 30
             df = 1
      sig.level = 0.05
          power = 0.9695413

NOTE: N is the number of observations

I assume in this case DF is supposed to refer to the number of parameters? If that's correct, why is it also used as sample size minus parameters?

What software are you using? Or more exact how did you compute the power.
You can check here:
https://www.statskingdom.com/33test_power_regression.html
that increasing you sample size, increases you power.
