I’m a big fan of statistics. Other than being fun to play with and fun to illustrate, they serve a lot of important tasks for researchers. They can quickly identify which of 500 comparisons is statistically significant. They can offer data to show whether your brand users comprise 2 distinct groups of people or 7 distinct groups of people. They can offer data to show which price your consumers would refuse to pay.
But there are two ways to use statistics. The right way and the wrong way. Or, as I like to say, to illuminate or to dictate.
The Wrong Way to Use Statistics – To Dictate
If you pay attention to statistics, they will tell you EXACTLY will differences are significant. They will tell you EXACTLY how many segments of consumers you have. They will tell you EXACTLY which price your consumers would refuse to pay. If you use statistics to dictate your conclusions and recommendations, you thereby hand over your intuition about humans work to a computer program. You ignore random error. You ignore human mistakes. You… make mistakes.
The Right Way to Use Statistics – To Illuminate
On the other hand, you can use statistics to guide you, to point you towards interesting dilemmas, to allow you to see things you wouldn’t be able to see otherwise. When statistics tell you that there are EXACTLY 7 segments of consumers, you can use your inherent understanding of the human condition to realize that a specific bias or error or issue affected the results and that segments 3 and 4 are actually the same segment. You can figure out that among the 500 statistics tests you conducted, some of them were significant by chance – and you might even be smart enough to identify and ignore which ones are the false positives. Statistics are a great way to help you see numbers in a new light and, in the end, you ought to determine what should be highlighted.
Smart researchers use statistics not to dictate, but rather to illuminate. How do YOU use statistics?
Comment
I would disagree with the statement: "They will tell you EXACTLY how many segments of consumers you have" while in theory you have tools like the elbow chart and some homogenity metrics, there are cases where they become fairly useless and defining the right number of segments requires human input.
© 2019 Data Science Central ® Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
Most Popular Content on DSC
To not miss this type of content in the future, subscribe to our newsletter.
Technical
Non Technical
Articles from top bloggers
Other popular resources
Archives: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More
Most popular articles
You need to be a member of Data Science Central to add comments!
Join Data Science Central