To determine the strongest evidence of a cause-and-effect relationship between independent and dependent variables, let's analyze the provided options:

• Descriptive statistics: This approach involves summarizing or describing data. It does not assess causality but only presents data distributions, measures of central tendency, and variability. Therefore, it is not suitable for establishing cause-and-effect relationships.
• Non-experimental approach: This includes observational methods where the researcher does not manipulate variables but only observes them. This approach can indicate correlations but cannot definitively establish causality because it lacks control over confounding variables.
• Correlational approach: Similar to the non-experimental strategy, correlation studies assess the relationship between two variables. However, correlation does not imply causation. High correlation coefficients may suggest a strong association, but they do not prove that one variable causes changes in another.
• Experimental approach: Researchers control and manipulate one or more independent variables to observe the effect on the dependent variables. This method often includes random assignment and control groups, allowing researchers to isolate and identify causal relationships. Thus, it provides the strongest evidence for causation.

Given these explanations, the Experimental approach offers the most robust evidence of cause-and-effect relationships because it allows for direct manipulation and control over variables, minimizing the influence of outside factors and confounding variables. This ensures that observed changes in the dependent variable can be attributed to changes in the independent variable.

Thus, the correct answer is the Experimental approach.