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2-Tailed vs. 1-Tailed Tests: The purpose of a hypothesis test is to avoid being fooled by chance occurrences into thinking that the effect you are investigating (for example, a difference between treatment and control) is real. If you are investigating, say, the difference between an existing process and a (hopefully…View Full Description
A Priori Probability: A priori probability is the probability estimate prior to receiving new information. See also Bayes Theorem and posterior probability. Browse Other Glossary EntriesView Full Description
A-B Test: An A-B test is a classic statistical design in which individuals or subjects are randomly split into two groups and some intervention or treatment is applied – one group gets treatment A, the other treatment B. Typically one of the treatments will be a control (i.e. nothing new),…View Full Description
Acceptance Region: In hypothesis testing, the test procedure partitions all the possible sample outcomes into two subsets (on the basis of whether the observed value of the test statistic is smaller than a threshold value or not). The subset that is considered to be consistent with the null hypothesis is…View Full Description
Acceptance Sampling: Acceptance sampling is the use of sampling methods to determine whether a shipment of products or components is of sufficient quality to be accepted. Browse Other Glossary EntriesView Full Description
Acceptance Sampling Plans: For a shipment or production lot, an acceptance sampling plan defines a sampling procedure and gives decision rules for accepting or rejecting the shipment or lot, based on the sampling results. Browse Other Glossary EntriesView Full Description
Statistical Glossary Additive effect: An additive effect refers to the role of a variable in an estimated model. A variable that has an additive effect can merely be added to the other terms in a model to determine its effect on the independent variable. Contrast with interaction effect. Browse Other…View Full Description
Statistical Glossary Additive Error: Additive error is the error that is added to the true value and does not depend on the true value itself. In other words, the result of the measurement is considered as a sum of the true value and the additive error: where is the result…View Full Description
Agglomerative Methods (of Cluster Analysis): In agglomerative methods of hierarchical cluster analysis , the clusters obtained at the previous step are fused into larger clusters. Agglomerative methods start with N clusters comprising a single object, then on each step two clusters from the previous step are fused into a bigger…View Full Description
Aggregate Mean: In ANOVA and some other techniques used for analysis of several samples, the aggregate mean is the mean for all values in all samples combined, as opposed to the mean values of the individual samples. The term “aggregate mean” is also used as a synonym of the weighted…View Full Description