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Accounting for the dependence structure of the p-values (or of the individual test statistics) produces more powerful procedures. This can be achieved by applying resampling methods, such as bootstrapping and permutations methods.
Post hoc analysis. In a scientific study, post hoc analysis (from Latin post hoc, "after this") consists of statistical analyses that were specified after the data were seen. [1][2] They are usually used to uncover specific differences between three or more group means when an analysis of variance (ANOVA) test is significant. [3]
Principal component analysis (PCA) is a widely used method for factor extraction, which is the first phase of EFA. [ 4 ] Factor weights are computed to extract the maximum possible variance, with successive factoring continuing until there is no further meaningful variance left. [ 4 ] The factor model must then be rotated for analysis. [ 4 ]
Analysis of variance. Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher.
Post hoc ergo propter hoc (Latin: 'after this, therefore because of this') is an informal fallacy which one commits when one reasons, "Since event Y followed event X, event Y must have been caused by event X." It is a fallacy in which an event is presumed to have been caused by a closely preceding event merely on the grounds of temporal succession.
The phrase " correlation does not imply causation " refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. [ 1 ][ 2 ] The idea that "correlation implies causation" is an example of a questionable-cause logical ...
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. [ 1 ][ 2 ] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output.
The Friedman test is a non-parametric statistical test developed by Milton Friedman. [1][2][3] Similar to the parametric repeated measures ANOVA, it is used to detect differences in treatments across multiple test attempts. The procedure involves ranking each row (or block) together, then considering the values of ranks by columns.