Internal validity

Internal validity is the extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study. It is one of the most important properties of scientific studies and is an important concept in reasoning about evidence more generally. Internal validity is determined by how well a study can rule out alternative explanations for its findings (usually, sources of systematic error or 'bias'). It contrasts with external validity, the extent to which results can justify conclusions about other contexts (that is, the extent to which results can be generalized). Both internal and external validity can be described using qualitative or quantitative forms of causal notation. Inferences are said to possess internal validity if a causal relationship between two variables is properly demonstrated. A valid causal inference may be made when three criteria are satisfied: the "cause" precedes the "effect" in time (temporal precedence), the "cause" and the "effect" tend to occur together (covariation), and there are no plausible alternative explanations for the observed covariation (nonspuriousness). In scientific experimental settings, researchers often change the state of one variable (the independent variable) to see what effect it has on a second variable (the dependent variable). For example, a researcher might manipulate the dosage of a particular drug between different groups of people to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences. When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable (that is, when the researcher observes an association between these variables and can rule out other explanations or rival hypotheses), then the causal inference is said to be internally valid.