MCAT content library / Research & data
Follow a research question from its design and measurements to a supported conclusion. Connect sampling, controls, ethics, graphs, uncertainty, and evidence from multiple figures.
Choose a starting point
State what competing explanations predict differently, then identify the comparison that addresses the difference. Recover the sample, axes, units, and reference before deciding what the result supports.
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Design a study
Experimental Controls: What a Comparison Can Show
Use controls to separate a treatment effect from a change in the measurement, then distinguish causal inference from population generalization.
12 min · Worked example · Original checks · Answer-specific feedback
Psychological Measurement: What Does the Score Mean?
Separate the attribute researchers want to study from the score they collect, then evaluate consistency, validity, bias and causal limits.
12 min · Worked example · Original checks · Answer-specific feedback
Hypotheses, predictions, and competing explanations
Choose experiments that make competing explanations predict different results.
14 min · Worked example · Original checks · Answer-specific feedback
Observational and experimental studies: what can we conclude?
Match a causal claim to group formation, measurement timing, and the alternatives the design leaves open.
14 min · Worked example · Original checks · Answer-specific feedback
Sampling and generalizability: trace who enters the estimate
Identify who could enter a study, who responded, and which population the resulting estimate can reasonably describe.
14 min · Worked example · Original checks · Answer-specific feedback
Cross-sectional and longitudinal studies: separate age, cohort, and change
Recover who was measured and when before interpreting an age difference, a changing mean, or a claim about development.
14 min · Worked example · Original checks · Answer-specific feedback
Blinding and expectancy: identify who knows what
Locate how knowledge of a condition could change behavior or measurement, then choose the comparison or safeguard that addresses it.
14 min · Worked example · Original checks · Answer-specific feedback
Surveys, interviews, and ethnography: choose evidence for the question
Choose a method by the information a question needs, then keep the conclusion within what the resulting observations can show.
14 min · Worked example · Original checks · Answer-specific feedback
Research ethics: match the safeguard to the problem
Identify the decision that threatens participants or the research record, then select a safeguard that directly addresses it.
14 min · Worked example · Original checks · Answer-specific feedback
Interpret data
Reading graphs: axes, scales, and the comparison that matters
Translate a graph into quantities and comparisons that stay valid when its display changes.
14 min · Worked example · Original checks · Answer-specific feedback
Mean, median, and spread: describing a dataset
Describe the center, spread, and tails of a dataset without losing the differences that matter.
14 min · Worked example · Original checks · Answer-specific feedback
Normalized data, percent change, and fold change
Recover the numerator, denominator, and reference behind a normalized result.
14 min · Worked example · Original checks · Answer-specific feedback
Accuracy, precision, and sources of measurement error
Use a reference and repeated readings to decide whether a measurement problem concerns its center, its spread, or both.
15 min · Worked example · Original checks · Answer-specific feedback
Correlation and scatterplots: inspect shape and hidden groups
Interpret paired data by its shape, groups, and sampled range before summarizing it with a correlation coefficient.
14 min · Worked example · Original checks · Answer-specific feedback
Confidence intervals, error bars, and sample size
Identify what the bars represent, which units are independent, and which interval answers the comparison.
16 min · Worked example · Original checks · Answer-specific feedback
Statistical significance and effect size: what a result means
Evaluate the estimated effect, its uncertainty, and the study design before interpreting a significance label.
16 min · Worked example · Original checks · Answer-specific feedback
Connect evidence
Reading a multi-figure experiment from claim to conclusion
Keep the question, readout, and control attached to each result before combining an experiment’s panels.
17 min · Worked example · Original checks · Answer-specific feedback
Study paths
Read a study and its figures
Follow a question through its design, graph, normalization and statistical description.
- Hypotheses, predictions, and competing explanations
- Experimental Controls: What a Comparison Can Show
- Observational and experimental studies: what can we conclude?
- Reading graphs: axes, scales, and the comparison that matters
- Normalized data, percent change, and fold change
- Mean, median, and spread: describing a dataset
From a dataset to a supported claim
Interpret variability and uncertainty before combining evidence from multiple figures.
- Reading graphs: axes, scales, and the comparison that matters
- Mean, median, and spread: describing a dataset
- Accuracy, precision, and sources of measurement error
- Confidence intervals, error bars, and sample size
- Statistical significance and effect size: what a result means
- Reading a multi-figure experiment from claim to conclusion
Connect the ideas
Use a related lesson to test the same reasoning in a different context. Return to the worked example after you have committed to an explanation, and use the answer feedback to identify the step that needs attention.
- Psychological Measurement: What Does the Score Mean?
- Measuring Gene Expression and Testing Gene Function
Use the lessons in your study plan
During Stage 1: Core Concepts, build an explanation you can state in your own words. During Stage 2: Practice-Based Learning, answer the checks before opening feedback, then try a changed application. Keep a note of the specific reasoning step you want to use on the next passage.
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