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.
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Ce cours couvre les fondements des systèmes numériques. Sur la base d'algèbre Booléenne et de circuitscombinatoires et séquentiels incluant les machines d'états finis, les methodes d'analyse et de syn
The course provides an introduction to econometrics. The objective is to learn how to make valid (i.e., causal) inference from economic and social data. It explains the main estimators and present met
In the behavioral sciences, ecological validity is often used to refer to the judgment of whether a given study's variables and conclusions (often collected in lab) are sufficiently relevant to its population (e.g. the "real world" context). Psychological studies are usually conducted in laboratories though the goal of these studies is to understand human behavior in the real-world. Ideally, an experiment would have generalizable results that predict behavior outside of the lab, thus having more ecological validity.
External validity is the validity of applying the conclusions of a scientific study outside the context of that study. In other words, it is the extent to which the results of a study can be generalized to and across other situations, people, stimuli, and times. In contrast, internal validity is the validity of conclusions drawn within the context of a particular study. Because general conclusions are almost always a goal in research, external validity is an important property of any study.
In causal inference, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation.
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