False discovery rateIn statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses) that are false (incorrect rejections of the null). Equivalently, the FDR is the expected ratio of the number of false positive classifications (false discoveries) to the total number of positive classifications (rejections of the null).
Family-wise error rateIn statistics, family-wise error rate (FWER) is the probability of making one or more false discoveries, or type I errors when performing multiple hypotheses tests. John Tukey developed in 1953 the concept of a familywise error rate as the probability of making a Type I error among a specified group, or "family," of tests. Ryan (1959) proposed the related concept of an experimentwise error rate, which is the probability of making a Type I error in a given experiment.
False coverage rateIn statistics, a false coverage rate (FCR) is the average rate of false coverage, i.e. not covering the true parameters, among the selected intervals. The FCR gives a simultaneous coverage at a (1 − α)×100% level for all of the parameters considered in the problem. The FCR has a strong connection to the false discovery rate (FDR). Both methods address the problem of multiple comparisons, FCR from confidence intervals (CIs) and FDR from P-value's point of view. FCR was needed because of dangers caused by selective inference.
False positive rateIn statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm ratio) is the probability of falsely rejecting the null hypothesis for a particular test. The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.
Multiple comparisons problemIn statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. The more inferences are made, the more likely erroneous inferences become. Several statistical techniques have been developed to address that problem, typically by requiring a stricter significance threshold for individual comparisons, so as to compensate for the number of inferences being made.
Correction de BonferroniEn statistiques, la correction de Bonferroni est une méthode pour corriger le seuil de significativité lors de comparaisons multiples. La correction de Bonferroni est la méthode de correction la plus simple, bien qu'elle soit conservatrice étant donné qu'elle présente un risque conséquent d'. En effet, cette méthode ne prend pas en compte quelques informations, comme la distribution des valeurs p des différentes comparaisons.
False positives and false negativesA false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a and a ).
Signification statistiquevignette|statistique En statistiques, le résultat d'études qui portent sur des échantillons de population est dit statistiquement significatif lorsqu'il semble exprimer de façon fiable un fait auquel on s'intéresse, par exemple la différence entre 2 groupes ou une corrélation entre 2 données. Dit autrement, il est alors très peu probable que ce résultat apparent soit en fait trompeur s'il n'est pas dû, par exemple, à un , trop petit ou autrement non représentatif (surtout si la population est très diverse).
Harmonic mean p-valueThe harmonic mean p-value (HMP) is a statistical technique for addressing the multiple comparisons problem that controls the strong-sense family-wise error rate (this claim has been disputed). It improves on the power of Bonferroni correction by performing combined tests, i.e. by testing whether groups of p-values are statistically significant, like Fisher's method. However, it avoids the restrictive assumption that the p-values are independent, unlike Fisher's method.
Data dredgingvignette|Exemple de Data dredging. Le data dredging (littéralement le dragage de données mais mieux traduit comme étant du triturage de données) est une technique statistique qui . Une des formes du data dredging est de partir de données ayant un grand nombre de variables et un grand nombre de résultats, et de choisir les associations qui sont « statistiquement significatives », au sens de la valeur p (on parle aussi de p-hacking).
