Summary Analyzing ecological experiments with many response variables is challenging because most statistical tools have low efficiency with such a design. It is widely known that introducing statistical control, i.e. testing one variable for significance while other response variables are held constant, increases the power of the analysis. Here we propose a very simple way to include statistical control to analyze experiments and show through simulations how, at least in simple experiments, the variable power rate (i.e. the power to detect an effect for each response variable) may increase 4× using our approach vs. using ANOVAs following a significant MANOVA. For simple experiments (i.e. one experimental treatment and a control) we propose the use of what we call Inverse Logistic Regression (ILR), named such because relative to the experimental design, the roles of dependent and independent variables become reversed. Using this approach one can obtain a multivariate p-value (i.e. full model) and univarite p-values (single variables) with a single analysis. In addition, our simulations show that when out of many possible response variables only a few correlated variables respond strongly to the treatment, the multivariate statistical power is doubled relative to that of MANOVA by iterative exploratory searching for the best subset of response variables using ILR. We also give an example of ILR with data from a field experiment in which ILR is more likely than MANOVA/ANOVA to uncover the pattern of treatment effects. Our approach can be extended to more complex unifactorial designs by using ordinal and/or multinomial distributions in generalized linear models or linear discriminant analysis when assumptions are met.