Many organizations have known the fact that data have been evolved from the by-product of corporate applications into a strategic asset . Like other corporate assets, the asset requires specialized skills to maintain and analyze. With modern data analytic tools, for example Python, R, SAS and SPSS, IT professionals can build models and uncover previous unknown knowledge from the ocean of data.
Every index has a matching statistic with the same name, and each statistic is a single 8KB page of metadata that describes the contents of your indexes. Stats have been around (and been mostly the same) for forever, so this is one of the places where SQL Server’s documentation really shines: Books Online has a ton of information about statistics. You honestly don’t need to know most of that – but if you wanna make a living performance tuning, there’s a ton of great stuff in there.
The use of statistics in SQL Server is tightly embedded in the query optimizer and query processor. The creation and maintenance of statistics is usually handled by the SQL Server engine, though many DBAs and developers know that periodically we might need to update those statistics to ensure good performance of queries. SQL Server 2019 gives us more options.
Many undergraduates have misunderstood the name 'Students' in the t-test to imply that it was designed as a simple test suitable for students. In fact it was William Sealy Gosset, an Englishman publishing under the pseudonym Student, who developed the t-test and t distribution in 1908, as a way of making confident predictions from small sample sizes of normally-distributed variables. As Gosset's employer was Guinness, the brewer, Phil Factor takes a sober view of calculating it in SQL.