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A few points to consider Generally, a 5-point Likert-scale data provides a poor approximation of continuous measurement in the first place. So note that calculating the normality of individual ordinal variables is usually a dismal and questionable practice. Having said that, for each of your subconstructs you essentially calculate scale averages, so each of ...


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What I am looking for turned out to be either a tolerance interval or a prediction interval.


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Suppose you have three different kinds of media outlets and that delays in coverage are exponential. None of the most common tests is ideal: (a) A standard ANOVA would not be idea because data are not normal and because variances are not the same (if populations differ). A Welch ANOVA test (oneway.test in R) would accommodate to differences in variances. ...


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Mood's median test gets funky in some cases and shouldn't be used in those situations. It sounds like you have two samples with the same median which is also equal to the pooled median. The first thing to say is that it would be ridiculous to say that two samples have statistically different medians when they have numerically the same median. That's a ...


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Here is an example that may be sufficiently similar to yours for a helpful explanation. My two groups have the same sample sizes, which are much smaller than the sample sizes in your problem. But the fundamental idea should be clear. Different distributions can produce samples that have the same median. Two groups x1 and x2 each have 50 observations (...


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Recently we have suggested a fast consistent mode estimator: P.S. Ruzankin and A.V. Logachov (2019). A fast mode estimator in multidimensional space. Statistics & Probability Letters Besides, there is a theme on this site devoted to fast mode estimators: Computationally efficient estimation of multivariate mode


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Recently we have published a paper with a fast mode estimator for multidimensional unimodal distributions. https://doi.org/10.1016/j.spl.2019.108670 That is not the answer to this question, since we estimated the mode only, not the whole density. Besides, our algorithm needs some simple corrections to be applicable for the dimensionality $d=20$. However that ...


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The analysis you are proposing sounds interesting, but the data collection process will be quite complicated. There are a few main issues you are going to have to deal with: Determine the scope of events of interest: Ideally you should determine the scope of events of interest to you (even in just a broad way) before you begin collecting the data. This ...


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This is an interesting investigation because of the flash-pan nature of the event. It's not the same as, say, installing a fence and trying to see if the number of trespassers was reduced. In that case, after the fence was installed, we would expect to see a permanent impact (if there was any) on the rate of trespassers. In this case, a bunch of mods will ...


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After speaking with a statistician, he advised me to : Conduct two Friedman tests, one for the experimental and another for the control group. These two different tests are needed because a single Friedman test cannot be used on two independent groups. The test can be applied to only one independent group. However, it is possible to compare the results of ...


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A general way to compare counts is in the context of a generalized linear model (GLM). Specifically, you could use a Poisson, or negative binomial GLM of the form: GLM1 <- glm(number_of_eqtles_per_gene ~ a_or_b, family = "poisson") # Poisson GLM2 <- glm.nb(number_of_eqtles_per_gene ~ a_or_b) # negative binomial Since you have plenty of observations, ...


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