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.
Internal validity is determined by how well a study can rule out alternative explanations for its findings (usually, sources of systematic error or 'bias').
Inferences are said to possess internal validity if a causal relationship between two variables is properly demonstrated.
In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the study.
When considering only Internal Validity, highly controlled true experimental designs (i.e. with random selection, random assignment to either the control or experimental groups, reliable instruments, reliable manipulation processes, and safeguards against confounding factors) may be the "gold standard" of scientific research.
Where spurious relationships cannot be ruled out, rival hypotheses to the original causal inference may be developed.
Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome.
For example, sex, weight, hair, eye, and skin color, personality, mental capabilities, and physical abilities, but also attitudes like motivation or willingness to participate.
During the selection step of the research study, if an unequal number of test subjects have similar subject-related variables there is a threat to the internal validity.
Events outside of the study/experiment or between repeated measures of the dependent variable may affect participants' responses to experimental procedures.
that affect participants' attitudes and behaviors such that it becomes impossible to determine whether any change on the dependent measures is due to the independent variable, or the historical event.
If any instrumentation changes occur, the internal validity of the main conclusion is affected, as alternative explanations are readily available.
If this attrition is systematically related to any feature of the study, the administration of the independent variable, the instrumentation, or if dropping out leads to relevant bias between groups, a whole class of alternative explanations is possible that account for the observed differences.