>>variance While it is true that, in many cases, variance does provide a good deal of information, particularly for statistical analysis, in many situations e.g. when the data is not normal, there are only a few points in a data set, or one wishes to examine only a single data set many other properties can be considered.
Specifically, it is often very useful to look at the median as well as the interquartile range. Quickly, just in case, the median is, after the data is sorted, the middle number. The inner quartile range is the difference between the value at the 75th percentile and the 25% i.e the range of the middle 50%. What is nice about these two values is that they eliminate outliers (numbers which are, for whatever reason, exceptionally large or small compared to the data set) and gives a better idea of where the data lies. The mean cannot account for large outliers and, for small data sets, can differ significantly from the median. While statistical analysis is more limited with the median, it can often be a more accurate representation of a population.
As an example, income reports very dramatically when looking at the difference between variance and inner quartile range. Because the median US income is far below the mean income (i.e. there are a small group of VERY wealthy people, thus the mean is pushed above the median) the inner quartile range is more informative that the variance. This is especially true on the micro level when e.g looking by county.
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It is a measure of the spread of the data around its mean value.
The mean of a distribution is a measure of central tendency, representing the average value of the data points. In this case, the mean is 2.89. The standard deviation, which measures the dispersion of data points around the mean, is missing from the question. The standard deviation provides information about the spread of data points and how closely they cluster around the mean.
The standard deviation is a measure of the spread of data about the mean. Although it is essentially a measure of the spread, the fact that it is the spread ABOUT THE MEAN that is being measured means that it does depend on the value of the mean. However, the SD is not affected by a translation of the data. What that means is that if I add any fixed number to each data point, the mean will increase by that number, but the SD will be unchanged.
Standard deviation shows how much variation there is from the "average" (mean). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values.
The mean and standard deviation often go together because they both describe different but complementary things about a distribution of data. The mean can tell you where the center of the distribution is and the standard deviation can tell you how much the data is spread around the mean.