Topic VI. Correlation and Causation
Filtering on
Context for this filter:
-
LEARNING GOALS
- B. CONCEPT ACQUISITION
- Randomized Controlled Trial (RCT): An attempt to identify causal relations by randomly assigning subjects into two groups and then performing an experimental intervention on the subjects in one of the groups.
-
EXAMPLES
- Exemplary Quotes
- “Even if we don’t know how, this seems to work. I know it seems crazy that you can fix this educational problem of delayed reading simply by feeding cereal to the kids every morning, but this was a pretty impressive randomized controlled trial so it’s hard to come up with another explanation.”
- Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
- Misconceptions of Induction
- "Aspirin is no better than a placebo. I used to give my sister bread pills when she asked me to get her aspirin, and she always said how much better they made her feel and never noticed the difference."
- "Our analyses of 833 diverse middle schoolers found that most of them learned better with hands-on activities. But we can't conclude anything about children who weren't in our study, because we didn't collect any data about them."
- "Eighty percent of people who took the drug got better. But the drug didn't really work, because a quarter of people who took the placebo got better even though they didn't take the drug, and twenty percent of people who took the drug didn't get any better at all."
- "RCTs cannot give sufficient evidence for causation that salt causes heart disease, because there might be other factors at play."
- Misconceptions of Control Condition
- "We should give the experimental drug to all eight hundred people in a study, instead of giving it to just half of them, because we want to maximize our sample size."
- "If you give a drug to 500 people with a disease, and 80% of them get better, then we know the drug works."
- Misconceptions of Randomization
LEARNING GOALS
- B. CONCEPT ACQUISITION
- Randomized Controlled Trial (RCT): An attempt to identify causal relations by randomly assigning subjects into two groups and then performing an experimental intervention on the subjects in one of the groups.
- a. Experimental Intervention: The act of an experimenter changing one variable in a possible causal network.
- b. Randomized Assignment: Given sufficient sample size, randomized assignment rules out confounds by distributing variation randomly between the two groups, thereby avoiding systematic differences except as the result of the intended intervention.
- c. Control Condition: Comparison of an experimental to a control condition is necessary in order to distinguish effect of intervention from changes that would have occurred without the intervention.
- d. Sampling: A study of a well-chosen sample can tell you something about the population (through induction), especially if it was selected in such a way as to avoid any systematic differences between the sample and the rest of the population. It is often difficult or even impossible to capture a perfectly representative sample, so scientists do the best they can. For example, many psychology studies are done with college students because they are accessible, but such samples differ systematically from the general population. Inferences from samples to a larger population need to take such differences into account.
- C. CONCEPT APPLICATION
- State some of the basic problems in establishing causation.
- Recognize some of the basic problems in establishing causation and use them to identify situations in which claims of causation are and are not warranted.
- Explain why a randomized controlled trial can help rule out spurious correlations.
- Address the argument, “Science can only establish correlations; it can’t determine causality.”
- Design RCTs for sample problems.
- Identify flaws in experimental designs aimed at testing causality and explain how the flaws could be addressed.
-
LEARNING GOALS
- C. CONCEPT APPLICATION
- For a given causal hypothesis and imperfect study, identify the imperfections (e.g., sample size, lack of randomization, lack of control) and explain how these imperfections impact claims of causality.
- Identify potential confounds in RCT and non-RCT studies.
- Sketch out methods for eliminating potential confounds in sample RCT or non-RCT studies.
- For a given scenario in which a causal hypothesis is being made, describe an ideal experiment/set of experiments to test the hypothesis and rule out alternative hypotheses.
-
EXAMPLES
- Cautionary Quotes: Mistakes, Misconceptions, & Misunderstandings
- Some students think that an RCT can demonstrate singular causation, whereas in fact it can only establish general causation.
- "This drug will absolutely cure you, because there was a really big RCT that showed it works for your exact disease!"