Bunny.Hop uses binary search, which assumes that the power function p:SampleSize->[0,1] is increasing.
If you use a null model, the goal is to find a sample size where your test will only detect something less than (1-power)% of the time. So Hop won't work:
Bunny.Hop(Experiment,limit=100,power=0.05)
Power: 0.05
Limit: 100
Replications per proposal: 10000
Searching for your sample size...
Simulating with 50 participants per condition... Power=0.0577
Simulating with 25 participants per condition... Power=0.0631
Simulating with 13 participants per condition... Power=0.0812
Simulating with 7 participants per condition... Power=0.1178
Simulating with 4 participants per condition... Power=0.1809
Simulating with 2 participants per condition... Power=0.4001
Simulating with 1 participants per condition... Power=0.9058
[1, 0.9]
Can't fix this if we want Bunny to be able to do binary search. Instead, Bunny.Hop() now checks if it converged. If it didn't it suggests running Bunny.Explore() or increasing the sample size.
Bunny.Hop uses binary search, which assumes that the power function p:SampleSize->[0,1] is increasing.
If you use a null model, the goal is to find a sample size where your test will only detect something less than (1-power)% of the time. So Hop won't work: