This information is often worse than no information, in this post I want to demonstrate two things.There are many people here in the Vollkin who are handy with Photoshop but also that averages can lie. Even if the underlying data is 100% accurate.
For example if I told you that I was very involved with charitable giving lately and we were delivering Christmas presents for children.
How involved? If you believe in numbers from NORAD my partner and I personally delivered presents in average over 750 million homes per year. And it’s quite exhausting cause to do that flew in average 36 million miles, exhausting indeed.
And for the moment let’s agree for go discussions that my partner existence and focus on my stats. My math is accurate but if the statements are actually misleading, considering the fact that Christmas last year fell within a week due date to my son’s birthday so you can bet I wasn’t out delivering presents 1000 miles away, in fact I barely left the house. But then spent 750,000 houses and 36,000,000 miles is still true because my partner did all the work and I just taken the credit being the average.
It’s maybe not actually example but see similar types of this phenomenal all the time in web analytic where averages can lie and mislead even the numbers are completely accurate.
For example if you have 100 visitors to your site and 99 of them than do thing but a next one spends $1000 would it be accurate to say that on average visitors to their site tend to spend $10 per visit? Well yes technically it’s true but it leads us to conclude the wrong thing entirely in our analysis of visitor behavior. Averages and abrogates have their place, the most often the real insides lie when we can segment out the roots of visitors and behavior then we can see who really driving success and who just using averages to take the credit.