If you have a question about how your process or organization is performing, statistics will be the methodology for answering those questions.

Statistics is the discipline and science used for the collection, organization, analysis, interpretation, and presentation of your data. Statistics usually fall into one of two categories:

- Descriptive statistics: methods used to summarize or describe a collection of data
- Inferential statistics: methods used to draw inferences about the process or population being studied

## Overview: What is statistics?Â

Descriptive statistics is a branch of statistics that deals with the collection, analysis, and interpretation of data that describes a group or population. Its main purpose is to summarize and present the data in a meaningful way, so that it can be easily understood and analyzed.

Descriptive statistics can be used to describe the central tendency, variation, and distribution Â or shape of a set of data. The central tendency is a measure of the mean or average value of the data, and can be represented by measures such as the mean, median, and mode. The variability of the data refers to how spread out the data points are from the central tendency, and can be measured using such calculations as the range, variance, and standard deviation. The distribution of the data refers to how the data is spread out over the entire range of possible values, and can be described using histograms, frequency tables, and other graphical tools.

Inferential statistics involves making inferences about a population based on sample data. This involves using statistical techniques to estimate population parameters. These techniques include hypothesis testing, confidence intervals, and regression analysis, among others.

It is important to point out that inferential statistics involve making probabilistic statements, rather than definitive ones. This means that the conclusions drawn from inferential statistics are not necessarily true with 100% certainty but are based on the probability of the results occurring by chance.

## An industry example of statisticsÂ

Inferential statistics is used in a wide range of applications, including medical research, public policy, marketing, and many other fields. For example, a pharmaceutical company might use inferential statistics to test the effectiveness of a new drug by comparing the results of a clinical trial with a control group.

A marketing organization may use hypothesis testing to compare the results of two marketing programs on social media. They may also use regression analysis to identify which factors are related to a change in sales. The same group might use descriptive statistics to describe the central tendency, variation and shape of their data for sales in the Northeast. Finally, they may use hypothesis testing to analyze whether there is any statistically significant difference in customer complaints between their 4 stores.

## Frequently Asked Questions (FAQ) about statistics

### What is the difference between descriptive and inferential statistics?

Descriptive statistics summarize and describe the characteristics of a sample or population, while inferential statistics use sample data to make generalizations or predictions about a population.

### What are some common statistical methods?Â

Some common statistical methods include hypothesis testing, regression analysis, correlation analysis, analysis of variance (ANOVA), and t-tests.

### How do I choose the appropriate statistical test for my data?Â

Choosing the appropriate statistical test depends on the type of data you have, the research question you are trying to answer, and the level of measurement of your data.Â

### What are some common errors in statistical analysis?Â

Common errors in statistical analysis include sampling bias, confounding variables and making type I and type II errors.Â