GED · Science · 6 min read
Designing and Evaluating Life Science Studies
- Distinguish between correlation and causation, and identify how confounding variables and study designs impact scientific conclusions.
- Identify independent, dependent, and controlled variables, and explain the functions of positive controls, negative controls, and replication in experimental setups.
- Evaluate sampling methods, blinding techniques, sources of bias, and empirical data to assess the validity of scientific claims.
Understanding Correlation, Causation, and Investigation Types
In life science, researchers frequently investigate how different factors connect to one another. A fundamental distinction in scientific inquiry is the difference between correlation and causation. Correlation describes a statistical relationship or association between two variables. When two variables correlate, changes in one correspond with changes in another. However, causation means that one variable produces a change in another, either directly or indirectly. The existence of a correlation between two variables does not prove that one caused the other.
A common challenge in evaluating relationships is the presence of a confounding variable. A confounding variable is a factor, whether measured or unmeasured, that is associated with both the presumed cause and the outcome and can distort their apparent relationship. Uncontrolled confounding variables can weaken a causal conclusion because they may offer an alternative explanation for the observed outcome. Similarly, researchers must watch out for coincidental trends, which occur when two unrelated variables change in similar patterns purely by chance over time.
Establishing true causation requires meeting specific scientific criteria. To establish causation, the cause must demonstrably occur before the effect in time. Researchers must also rule out reverse causation, which occurs when the presumed outcome is actually the cause of the presumed risk factor. A plausible biological mechanism can strengthen a causal explanation, but establishing a causal link does not always require a known mechanism or a direct effect.
The type of study conducted also determines what conclusions can be drawn. Observational health studies detect associations but cannot conclusively establish direct causation. In contrast, experimental studies deliberately manipulate a variable and control other relevant factors as much as possible to test for causal effects.
Common mistakes
- Assuming that a strong correlation proves one variable directly caused changes in another, ignoring confounding variables, reverse causation, or coincidence.
- Believing that keeping relevant factors constant guarantees that no other variable influenced the experimental outcome.
- Arbitrarily deleting outlier data points to make the results match an initial hypothesis instead of investigating them for measurement error.
- Assuming that non-overlapping error bars on a graph automatically prove statistical significance without checking what the bars represent and the statistical analysis.
Key points
- Correlation indicates an association between variables, whereas causation requires that one variable produces a change in another; observational studies detect associations, while experimental studies manipulate variables to test causal effects.
- Independent variables are changed by researchers, dependent variables are the measured outcomes, and controlled variables are kept constant across groups as much as possible.
- Control groups establish a baseline: negative controls receive no active treatment or an ineffective treatment, while positive controls receive a treatment known to produce the outcome.
- Random sampling gives all population members an equal selection chance, while random assignment distributes differences between experimental and control groups.
- Placebos help account for effects of participant expectations, and double-blind designs reduce, but do not necessarily eliminate, bias from participant and researcher expectations.
- Scientific claims require empirical evidence rather than claims of absolute proof, and hypotheses that are repeatedly contradicted by data must be revised or rejected.
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