Duplicate Statistics Search and Destroy

This script finds auto created statistics that duplicate existing stats generated by indexes, and may help to speed up your statistics maintenance. Copy the results from the DropScript column to a new SSMS tab and execute.

Customizing Statistics Histogram in SQL Server 2019

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.

Auto Created Statistics

Finding & Fixing Statistics Without Histograms

Men Without Hats were awesome. Statistics without histograms are terrible, but now they're easy to find and fix.

Statistics not updated for the last 30 days

Find out-of-date statistics for 30 days

Introduction to Statistics

This article will give a brief overview of how statistics are generated, stored, and used in SQL Server.

Statistics in SQL: Student’s t-test

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.

Pattern Recognition via Principal Components Analysis

We'll look at using principal components analysis to help visualise your data and detect underlying structure or patterns.

Statistics in SQL: The Mann–Whitney U test

Phil Factor shows how to use the Mann-Whitney U test in SQL to to find out whether two samples come from the same distribution.

Statistics in SQL: The Kruskal–Wallis Test

Before you report your conclusions about your data, have you checked whether your 'actionable' figures occurred by chance? The Kruskal-Wallis test is a safe way of determining whether samples come from the same population, because it is simple and doesn't rely on a normal distribution in the population. This allows you a measure of confidence that your results are 'significant'. Phil Factor explains how to do it.