Statistics for Business Intelligence – Introduction

An understanding of Statistics is imperative before delving into the tools and methods of business intelligence and analysis. We will spend some time on understanding the basics of Statistics for data analysis and in subsequent posts try to give more detailed explanation of the various methods involved.
Statistics for data analysis can be broadly divided into descriptive statistics and inferential statistics.

Descriptive statistics is a group of methods or tools that statisticians use to understand the meaning of data. These methods provide a summary or gist of data and enable the user to comprehend large volumes of data. For example, the average salary for a class of MBA pass out or the median of weight in a group of people are examples of descriptive statistics.

Inferential statistics, on the other hand, provides tools that statisticians use to infer information about the population given the information about the sample of the population. In other words it tries to use the available data to understand how the population would look like. Consider for example a survey that finds out popularity of a TV show. A group of people are selected at random and in a way that represents the population that the survey wants to cover (say a city). Descriptive statistics would give the summary of popularity within the people chosen for the survey but to extrapolate this findings for the complete population of the city, we need inferential statistics.