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Have you considered using the coefficient of variation (CV)? $CV=\frac{s}{\bar{x}}$ Where $s$ is the standard deviation and $\bar{x}$ is the mean. CV can be expressed as a percent.

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I would say you can use anova for analyzing noise level measurements, see this stored google search which links many papers using anova in the analysis of noise measurements. As you say, noise measurements in decibel cannot really be added, say, if the problem is finding the resultant noise level from two simultaneous independent sources, like a jet taking ...

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You should have given some context, what (real-life) variable $x$ do your data represent? Some questions you probably know answers for: What is the possible range for $x$? That is, is $x$ nonnegative? or a count? ... Can we suppose independence? Nevertheless, some observations: the mean is larger than the median, and a 95% confidence interval for the ...

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This is a heuristic for approximating skewness of the long-tail in non-negative valued distributions. It's used to signal changes in the tails. Without a minimum specified, you'll find it relevant to latency, time-to-resolve, and any log-normal distributions. With only non-negative values, and an assumed minimum of zero, the observed maximum is also the ...

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Not sure if it works as a solution, but I found the following lecture which seems to confirm your point 1 assumption: https://www.stat.cmu.edu/~ryantibs/datamining/lectures/18-val1.pdf

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@Sal Mangiafico is on the right track, so I'd just add my few cents to his answer. The phenomenon you are describing is called examiner effect, and you can google for this term to find more hints on solving this problem. Are you familiar with Item Response Theory? This is a theory, or rather a family of models used in psychometry for solving similar ...

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Hopefully this isn't overkill, but I think this is a case where creating a linear model, and looking at the estimated marginal means, would make sense. Estimated marginal means are adjusted for the other terms in the model. That is, the E.M. mean score for student A can be adjusted for the effect of Evaluator 1. That is, how much higher or lower Evaluator ...

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According to Moore's The Basic Practice of Statistics, "Call an observation a suspected outlier if it falls more than 1.5*IQR above the third quartile or below the first quartile." I would uses this as the gold standard myself. Thus, a value lying on 1.5*IQR below $Q_1$ would NOT be an outlier.

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One distribution that fits these parameters is a mixture of: 50% a lognormal distribution with $\mu=\ln 55,\ \sigma=.89$ 25% a point mass at $31.38$ 25% a point mass at $97.38$ So the quartiles could be at those point masses, though there are many other possibilities also. I found this by solving some equations; here is the explanation: The median of the ...

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As @Dave noted in a comment, you would have to make some assumptions about the distribution. Given the mean and the median being so different, it's likely that there is substantial skew - and you confirm this in a comment. Various assumptions might be reasonable. With median = 55 and IQR = 66 (and no other info or assumptions), then, with a symmetric ...

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The answer to your question depends on the purpose of your Table 1. One purpose of Table 1 would be to help your readers understand more about the subjects in whom you are investigating the association between smoking and plaque, adjusted for cofounders. In this case, Table 1 could include so-called demographic variables (e.g., age, gender) and possibly ...

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The most general answer: It depends. The crucial question is, what exactly do you mean by "voted similar". In statistics, often people tend to just use some given measure and then assume that it fits what they are testing. But it is important to first clarify what the terms mean. As a somewhat sophisticated example. Assume Party 1 and Party 2 are two types ...

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There is no unique answer to this. It depends on what you mean by "similarity", which one could say is implicitly defined by the measure you choose. Note that the data alone or statistical theory cannot tell you what "similarity" you should be interested in! Personally, for the given situation, I would not use Spearman or Kendall correlation, because this ...

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If you use the interquartile mean (or midmean), the maybe you should also report its standard error ? But, if what you want is some robust measure of spread of the data, then choose such a measure (maybe MAD?), preferably a known one. Your proposal of using the standard deviation of the data between the first and third quartile doesn't seem to have much ...

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For such data I would start out with visualization, in this case a Bland-Altman plot (or Tukey mean-difference plot.) For an example, see Bland & Altman plot for repeated measures using one measuring device. In this case, maybe plotting on the x axis the IQbefore, and on the y-axis the change score IQafter-IQbefore. Then color the points according to ...

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Theoretically speaking the outcomes of an experiment (experiment = a random procedure) can be numerical (i.e rolling a die) or can be mapped to numbers by the designer (i.e flipping a coin, with outcomes 1=head and 0=tail). This numerical representation of the outcomes defines the random variables. In these examples, we can tell that there is some chance ...

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A common way to measure inequality is the Gini coefficient as one of many metrics. However, depending on your concern and your business problems, you might want to define a metric on your own. For example, if the revenue concentration on too few customers causes problems in the financial balance of the company, one can estimate a turnover probability (...

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The SD line is a didactical and visual aid to help seeing the relation for the slope of the regular regression line. $$\text {slope regression } = r_{xy} \, \frac {\sigma_y}{\sigma_x} = r_{xy} \, \text {slope SD line}$$ The SD line shows how x and y are varying and this can give a more or less steep or flat line depending on the ratio \$ \frac {\sigma_y}{\...

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If your N is actually 4, there isn't much you can do. But if the data shown are just a sample, then here are some ideas: You could just look at the average of the trials vs. the true measure. You can improve your looking ability by using graphs. Certainly a scatter plot. Perhaps a quantile quantile plot and maybe a Tukey mean difference plot (aka Bland ...

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