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Validity is the main extent to which a concept, conclusion, or measurement is well-founded and likely corresponds accurately to the real world. [1] [2] The word "valid" is derived from the Latin validus, meaning strong.
External validity is the validity of applying the conclusions of a scientific study outside the context of that study. [1] In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times.
A single record in this table is referred to as an analytical record or analytic record (AR), and represents the subject of the prediction (e.g. a customer) and stores all data (variables) describing this subject. [2] If for example the subject is a customer then the record may be referred to as a customer analytic record or "CAR". [3] [4] [5]
Explicit table captions (or titles) are recommended for data tables as a best practice; the Wikipedia Manual of Style considers them a high priority for accessibility reasons (screen readers), as a caption is explicitly associated with the table, unlike a normal wikitext heading or introductory sentence. All data tables on Wikipedia require ...
MIL-STD-105 D Quick reference Table, TABLE I and TABLE IIA. MIL-STD-105 was a United States defense standard that provided procedures and tables for sampling by attributes based on Walter A. Shewhart, Harry Romig, and Harold F. Dodge sampling inspection theories and mathematical formulas. Widely adopted outside of military procurement applications.
The example above is the simplest kind of contingency table, a table in which each variable has only two levels; this is called a 2 × 2 contingency table. In principle, any number of rows and columns may be used. There may also be more than two variables, but higher order contingency tables are difficult to represent visually.
Lilliefors test is a normality test based on the Kolmogorov–Smirnov test.It is used to test the null hypothesis that data come from a normally distributed population, when the null hypothesis does not specify which normal distribution; i.e., it does not specify the expected value and variance of the distribution. [1]
In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.