Experimental Design Logic
Controls, confounders, and validity checks
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Experimental Design Logic
Variables and Controls
Every experiment manipulates an independent variable, measures a dependent variable, and holds confounds constant. A positive control shows the assay CAN produce a signal; a negative control shows it doesn't produce one spuriously. Questions love asking which control is missing.
Validity and Causation
Random assignment supports causal claims; correlation from observational designs does not. Watch for confounding variables (a third factor driving both), selection bias in sampling, and whether results generalize beyond the studied population (external validity).
Interpreting Outcomes
If the experimental group and control differ, the manipulation is implicated — but only if groups differed in just that one way. If a predicted effect fails to appear, either the hypothesis is wrong or the design lacked power or the right conditions. The best MCAT answer stays modest: supported, not proven.
Key Takeaways
- Name the IV, DV, and both control types in any described experiment
- Causation requires random assignment, not just correlation
- Missing-control and confound questions are the most common design items
- Conclusions should be the weakest claim the data fully support
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