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In statistics, there is a negative relationship or inverse relationship between two variables if higher values of one variable tend to be associated with lower values of the other. A negative relationship between two variables usually implies that the correlation between them is negative, or — what is in some contexts equivalent — that the ...
In statistics, homogeneity and its opposite, heterogeneity, arise in describing the properties of a dataset, or several datasets.They relate to the validity of the often convenient assumption that the statistical properties of any one part of an overall dataset are the same as any other part.
The term false discovery rate (FDR) was used by Colquhoun (2014) [4] to mean the probability that a "significant" result was a false positive. Later Colquhoun (2017) [ 2 ] used the term false positive risk (FPR) for the same quantity, to avoid confusion with the term FDR as used by people who work on multiple comparisons .
The minus–plus sign, ∓, is generally used in conjunction with the ± sign, in such expressions as x ± y ∓ z, which can be interpreted as meaning x + y − z or x − y + z (but not x + y + z or x − y − z). The ∓ always has the opposite sign to ±.
The word statistics ultimately comes from the Latin word Status, meaning "situation" or "condition" in society, which in late Latin adopted the meaning "state". Derived from this, political scientist Gottfried Achenwall, coined the German word statistik (a summary of how things stand).
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
Anarchy; Anarchist Black Cross; Anarchist criminology; Anationalism; Anti-authoritarianism; Anti-capitalism; Anti-militarism; Affinity group; Autonomous social center
A possible null hypothesis is that the mean male score is the same as the mean female score: H 0: μ 1 = μ 2. where H 0 = the null hypothesis, μ 1 = the mean of population 1, and μ 2 = the mean of population 2. A stronger null hypothesis is that the two samples have equal variances and shapes of their respective distributions.