Critiquing an Experiment: Caffeine and Alertness

Correlation is not causation: It's a common mistake, even in higher years, to assume correlation implies causation.
True Experiments and Causation: A strength of well-designed experiments is the ability, in principle, to infer a causation relationship. For example, if those who drank caffeine were theoretically more alert than those who didn't.
Strength of Conclusions: The strength of conclusions depends on the design features used or not used in the study.

Type of Design: The caffeine experiment used a between-groups design because the control group (no drink) was separate from the experimental groups (caffeine/decaf).
Random Assignment: The experiment did use random assignment to groups (caffeine, decaf). The control group, however, was self-selected (people choosing not to drink coffee at all), which is a potential issue.
- Implication of self-selection: There might be inherent differences in people who self-select not to drink coffee, which could confound results.
Placebo Effect: The decaf group served as a placebo control, where participants thought they might be receiving caffeine, but didn't.
Quasi-experiment vs. True Experiment: This was a true experiment due to random assignment (for caffeine/decaf groups).

Independent Variable (IV): The variable that is manipulated. In this experiment, it was caffeine (hypothesized to affect the dependent variable).
Dependent Variable (DV): The variable that is measured and hypothesized to be affected by the IV. In this experiment, it was alertness, operationalized as reaction time (measured by a ruler drop task).
Manipulation Uniformity: A critical question is whether the manipulation was uniform across participants.
- Issue: In the actual experiment, the manipulation was not uniform because participants chose different add-ins (milk, sugar) and varying amounts of coffee, rather than a standard $100 ext{ ml}$ of decaf or caffeinated coffee without add-ins.
Confounding Factors: Other potential causes that might influence the dependent variable.
- Examples: Differences in personal experiences (e.g., cold water immersion lecture example with varying exposure), time of day caffeine was consumed.
- Randomization as a solution: Random assignment helps to average out confounding factors across groups (e.g., an even number of early morning/late afternoon drinkers, fast/slow reaction times in each group), especially with larger sample sizes.
- Lingering Concerns: While randomization hopes to take care of issues like milk and sugar, or conditions like ADHD (where caffeine might calm rather than alert), it's not guaranteed without specific record-keeping.

Validity: The extent to which we are measuring exactly what we intend to measure.
- Example: For alertness, we're measuring how fast one catches a ruler, not milk consumption or