Get the facts straight: The 10 Most Common Statistical Blunders

Get the facts straight: The 10 Most Common Statistical Blunders

Get the facts straight: The 10 Most Common Statistical Blunders

Competent analysis is not only about understanding statistics, but about implementing the correct statistical approach or method. In this brief article I will showcase some common statistical blunders that we generally make and how to avoid them.

To make this information simple and consumable I have divided these errors into two parts:

This is one nightmare-inducing area to both the presenter as well as the audience. Incorrect data presentation can skew the inference and can leave the interpretation at the mercy of the audience.

Pie charts are considered to be the best graph when you want to show how the categorical values are broken. However, they can be seriously deceptive or misleading. Below are some quick points to remember when looking at the Pie Charts:

Bar graphs are great graphs to show the categorical data by the number or percent for a particular group. Points to consider when examining a Bar Graph:

A time chart is used to show how the measurable quantities change by time.

Read Also:
Apple aims to up its AI smarts with iCloud user data in iOS 10.3

This is probably a ‘no-nonsense zone’ where one would not want to make false assumptions or erroneous selections. Statistical errors can be a costly affair, if not checked or looked into it carefully.

Bias in statistics can be termed as over or underestimating the true value. Below are some most common sources or reasons for such errors.

This is a great way to understand the potential miscalculation or change in circumstance that can result in a sampling error and ensures that the result from a sample study is close to the number that can be expected from the entire population. It is a good idea to always look for this statistics to ensure that the audiences are not left to wonder about the accuracy of the study.



Read Also:
Welcome to the post-cloud future
Read Also:
What can data visualization learn from punks?
Read Also:
Quantum artificial intelligence could lead to super-smart machines, page 1
Read Also:
Pushing data quality beyond boundaries
Read Also:
Pushing data quality beyond boundaries

Leave a Reply

Your email address will not be published. Required fields are marked *