To make causal inferences from observed differences between groups based on varied levels of treatment, it is necessary to...?

A. Find significant differences between groups at at-least the .05 level
B. Use T-test or ANOVA procedures to conduct the analysis
C. Have random assignment of participants to treatment levels and control conditions as a basis of design, prior to conducting analysis
D. Deploy at least a quasi-experimental design

To make causal inferences from observed differences between groups based on varied levels of treatment, it is necessary to have random assignment of participants to treatment levels and control conditions as a basis of design, prior to conducting analysis (option C).

Random assignment is a key component of experimental design that helps reduce bias and confounding variables. By randomly assigning participants to different treatment levels and control conditions, we can ensure that any observed differences between groups are likely due to the treatment itself, rather than other factors. This randomization process helps create comparable groups, making it easier to establish causality between the treatment and the observed effects.

Options A and B are not sufficient on their own to establish causal inference. Finding significant differences between groups (option A) does not guarantee a causal relationship because it could be influenced by chance or other factors. Similarly, using statistical tests like T-test or ANOVA (option B) alone cannot establish causality, as they only assess the statistical significance of the observed differences.

Option D, deploying at least a quasi-experimental design, can provide some evidence for causal inference but is less rigorous than true experimental designs. Quasi-experimental designs lack random assignment, which can introduce bias and limit our ability to infer causality. Random assignment provides a stronger basis for causal inferences.