First of al, thank you for your time.
- My independent variable is the amount of computations that are performed.
- My dependent variable is the fit of the model (fitness)
- My control variables are the tuning settings of the genetic algorithm
For each of the 3 (independent variables) I do 10 repeated measurements where I initialize the algorithm at a random point. I hope this clarifies my approach. I don' t know how to approach random computer initialization as 'subjects': so I can't determine if it needs a within-subjects or between subjects ANOVA to prove the statistical relevance. What would independent repeated measurements be categorized (as between or within subjects)?
I had to optimize a Genetic algorithm and one of the plots include a trade-off between computational effort and convergence (see: https://imgur.com/1oAAJwA) For 3 given settings I performed 10 independent runs, where each outputed a certain fitness [0-1]. Now I want to compute the statistical relevance of the (black) trend. My questions is: how would you classify these kind of independent computer measurements (Between subjects or Within subjects)? I am using the book "Discovering Statistics Using SPSS by Andy Field" to define the required statistical tests, though this is where I get stuck. Normallity is met (One-sample Kolmogorov-Smirnov test), and sphericity clearly not: so it is iether a Friedman's ANOVA or a Kruskal-Wallis test according to this book. Although seeing that I don't really use human subjects but independent computer runs I am not sure if this is the way to go. note: the variances in the figure are huge, primarily because of limited sampling size: though a bigger one was not required for the course I'm doing. The 95% confidence intervals have been cut of at a fitness of 1, because they cannot occur beyond 1 (result of small sample size) )