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An example of histogram matching. In image processing, histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [1] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed. [2]
In particular cases, simpler tests like paired difference test, McNemar test and Cochran–Mantel–Haenszel test are available. When the outcome of interest is continuous, estimation of the average treatment effect is performed. Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression ...
The match-to-sample task has been shown to be an effective tool to understand the impact of sleep deprivation on short-term memory. One research study [9] compared performance on a traditional sequential test battery with that on a synthetic work task requiring subjects to work concurrently on several tasks, testing subjects every three hours during 64 hrs of sleep deprivation.
Consider two points p and q that have normalized K-bin histograms (i.e. shape contexts) g(k) and h(k). As shape contexts are distributions represented as histograms, it is natural to use the χ 2 test statistic as the "shape context cost" of matching the two points:
It is also common to use the multivariate η 2 when the assumption of sphericity has been violated, and the multivariate test statistic is reported. A third effect size statistic that is reported is the generalized η 2, which is comparable to η p 2 in a one-way repeated measures ANOVA. It has been shown to be a better estimate of effect size ...
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]
The Wilcoxon signed-rank test is a non-parametric rank test for statistical hypothesis testing used either to test the location of a population based on a sample of data, or to compare the locations of two populations using two matched samples. [1] The one-sample version serves a purpose similar to that of the one-sample Student's t-test. [2]
Figure 2: Graph showing histograms of person distribution (top) and item distribution (bottom) on a scale. For dichotomous data such as right/wrong answers, by definition, the location of an item on a scale corresponds with the person location at which there is a 0.5 probability of a correct response to the question.