Not much, it just gives you a first idea about them, but you always do need more information.
the midpoint of the data set
It tells you that that number has the most part of everything.
The mean tells the sum of the whole set of data.h
It is the set of values that a variable can take together with the probability or frequency distribution for those values.
The normal distribution allows you to measure the distribution of a set of data points. It helps to determine the average (mean) of the data and how spread out the data is (standard deviation). By using the normal distribution, you can make predictions about the likelihood of certain values occurring within the data set.
The mean alone does not provide any information about the higher order moments of the distribution of the data set. Most important amongst these is that it does not give information about the variance - or the spread of the distribution. The data set could be distributed in a narrow band about the mean or spread more evenly over a wider range. It does not tell you anything about the skewness, that is whether there are many values smaller than the mean balanced by a few large values (or the other way around). Moments of the fourth or higher order are difficult to comprehend and, in most cases, ignored.
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
The median and mean of a data set can be the same when the data is symmetrically distributed, such as in a normal distribution. In this case, the mean accurately reflects the central tendency of the data, and the median, being the middle value, aligns with it. However, in skewed distributions, the mean and median can differ significantly due to the influence of outliers. Thus, while they can be equal, it depends on the distribution characteristics of the data set.
A frequency distribution plot.
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
The answer will depend on the set of data!
it tells you