One such function is the SQL standard percentile_cont function, which takes the percentile as an argument, and then accepts an additional WITHIN GROUP clause that takes an ORDER BY clause as an argument. Interestingly, apart from window functions, you can also specify ORDER BY clauses to certain aggregate functions that aggregate data based on ordered sets. There is another way of calculating the MEDIAN() in particular and any sort of percentile in general in the SQL standard, and since PostgreSQL 9.4 also in PostgreSQL using вЂ¦ We can thus establish:ДЄll of the above are special cases of percentiles, and while MIN() and MAX() are supported in all SQL databases (and the SQL standard), MEDIAN() is not in the SQL standard and only supported by the following jOOQ databases: This particular value is also called the 50th percentile because 50% of all values in the sample are lower than the MEDIAN(). This blog is not about politics but about Java and SQL, so letвЂ™s get back into calculating the actual facts.ДЄs weвЂ™ve seen before, the MEDIAN() divides a sample into two equally-sized groups and takes the value вЂњbetweenвЂќ those two groups. This is due to wealth being heavily skewed towards the super-rich more and more. (and in many other countries) has been steadily increasing, the median income has seen a decline over the past decade. the median income in a country, for instance. The skewing effect is very important in statistics and in order to make any interesting claim about anything, using percentiles is most often more useful than using averages. The above sample is of course statistically insignificant, but you can easily see that the effect can be dramatic and relevant, if you have more data: Then your average will also be skewed, whereas your median will still indicate where most of the values are in your sample

Java median function