Ecological validityIn 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.
Validité externeLa validité externe d'une expérience scientifique désigne la capacité de ses conclusions à être généralisées à des contextes non-expérimentaux. Une expérience a une grande validité externe dès lors que ses résultats permettent de comprendre des phénomènes hors du laboratoire. À l'inverse, elle manque de validité externe si les conclusions que l'on peut en tirer ne sont valables que pour des conditions expérimentales restrictives.
Facteur de confusionEn statistique, un facteur de confusion, ou facteur confondant, ou encore variable confondante, est une variable aléatoire qui influence à la fois la variable dépendante et les variables explicatives. Ces facteurs sont notamment à l'origine de la différence entre corrélation et causalité (Cum hoc ergo propter hoc). En santé publique, c'est une variable liée à la fois au facteur de risque et à la maladie ou à un autre évènement de l'étude lié à la santé, ce qui est susceptible d'induire un biais dans l'analyse du lien (entre maladie et facteur de risque), produisant ainsi de fausses associations.
Statistical conclusion validityStatistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". This began as being solely about whether the statistical conclusion about the relationship of the variables was correct, but now there is a movement towards moving to "reasonable" conclusions that use: quantitative, statistical, and qualitative data. Fundamentally, two types of errors can occur: type I (finding a difference or correlation when none exists) and type II (finding no difference or correlation when one exists).
Statistical model validationIn statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data.
Spurious relationshipIn statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor (referred to as a "common response variable", "confounding factor", or "lurking variable"). An example of a spurious relationship can be found in the time-series literature, where a spurious regression is a one that provides misleading statistical evidence of a linear relationship between independent non-stationary variables.
Construct validityConstruct validity concerns how well a set of indicators represent or reflect a concept that is not directly measurable. Construct validation is the accumulation of evidence to support the interpretation of what a measure reflects. Modern validity theory defines construct validity as the overarching concern of validity research, subsuming all other types of validity evidence such as content validity and criterion validity.
Validity (statistics)Validity is the main extent to which a concept, conclusion or measurement is well-founded and likely corresponds accurately to the real world. The word "valid" is derived from the Latin validus, meaning strong. The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
ÉpidémiologieL'épidémiologie est une discipline scientifique qui étudie les problèmes de santé dans les populations humaines, leur fréquence, leur distribution dans le temps et dans l’espace, ainsi que les facteurs exerçant une influence sur la santé et les maladies de populations. L'étude de la répartition et des déterminants des événements de santé sert de fondement à la logique des interventions faites en matière de santé publique et de médecine préventive.
Essai randomisé contrôléUn essai contrôlé randomisé (ECR), , essai randomisé contrôlé (ERC), essai comparatif randomisé (ECR) (de l'anglais randomized controlled trial ou RCT), essai comparatif aléatoire ou encore essai contrôlé aléatoire (ECA) est un type d'étude scientifique utilisé dans de multiples domaines (psychologie, soins infirmiers, éducation, agriculture, économie) et en particulier en médecine où il occupe un rôle prépondérant.
