Which are requirements for causation?

Which are requirements for causation?

• A. Manipulation, control, and random assignment.
• B. Large sample size only.
• C. Cross-sectional design.
• D. Statistical significance alone.

Answer: (A)

To claim a causal relationship, you need to actively change the cause, ensure groups are comparable, and keep other factors from influencing the outcome. Manipulation means the researcher deliberately changes the independent variable and observes what happens to the dependent variable. Random assignment helps make the groups equivalent at the start so differences in outcomes are due to the manipulation rather than preexisting differences. Control involves holding or accounting for other variables that could affect the result, so you’re isolating the effect of the manipulated variable. That’s why this option is the best: it embodies the core elements that let researchers infer causation in experimental designs. Large sample size helps with precision but not causation itself. A cross-sectional design measures variables at one point in time, which makes it hard to establish that the cause came before the effect. Statistical significance alone indicates a relationship but doesn’t rule out confounding factors or alternative explanations.

To claim a causal relationship, you need to actively change the cause, ensure groups are comparable, and keep other factors from influencing the outcome. Manipulation means the researcher deliberately changes the independent variable and observes what happens to the dependent variable. Random assignment helps make the groups equivalent at the start so differences in outcomes are due to the manipulation rather than preexisting differences. Control involves holding or accounting for other variables that could affect the result, so you’re isolating the effect of the manipulated variable.

That’s why this option is the best: it embodies the core elements that let researchers infer causation in experimental designs. Large sample size helps with precision but not causation itself. A cross-sectional design measures variables at one point in time, which makes it hard to establish that the cause came before the effect. Statistical significance alone indicates a relationship but doesn’t rule out confounding factors or alternative explanations.