All these research studies!

Reading about claims/findings from many of these research studies often makes me shake my head in disbelief. The latest one I saw was titled "Scientists suggest beer after a workout". Below are some of the claims/statements from that article:

1. The study involved a group of students who were asked to work out until their body temperature reached 104 degrees. Researchers then gave beer to half of the students and water to the other half.

2. Professor Manuel Garzon announced the results at a press conference in Granada, saying the hydration effect in those who drank beer was “slightly better,”

3. And their conclusion? Researchers at Granada University in Spain have found that beer can help the body rehydrate better after a workout than water or Gatorade.

My questions:

1. How many students? How were they selected? How were they split in the two groups?

2. Slightly better? Really??

3. Wow, that was fast!!

You can read the full article here

So you thought...?

Sometimes you get the feeling that everyone around you is so confused or just don't know about things which are basic and essential in Analytics. Below is a list of the most common terms that a majority thinks they know but don't.

1. Linear/Pearson Correlation: The most misunderstood term as far as i know. Before doing anything else, check if the 2 variables share a linear relation. Correlation values without a linear pattern is meaningless. And also be aware that in many softwares (including MS Excel), the default is pearson correlation, for which a linear relation between the two variables is a requirement.

2. Significance Test: Many many people into Analytics (?) will never ever understand this or will never try to understand this. Just because you see 2 groups doesn't mean that you can do a significance test. Know something or everything about sampling and designs before talking about significance test.

3. Lift and Cumulative Gains Charts: They are different, period. Don't confuse one with another.





Lift - Without a model, we get 30% of the responders by contacting 30% of the customers. Using a model, we get 60% of responders. The lift is 60/30 = 2 times.

Cumulative Gains - Using the model, if we contact 30% of the customers we get 60% of all responders.

4. Clustering/Segmentation and Profiling: Let's make this simple. Clustering/Segmenting will answer - Can my customer base be broken up into distinct groups based on certain attributes/characteristics? Customers within a group will be very similar to one another while customers across groups will be different.

Profiling will answer - Who are my best customers? What do they purchase? How often? What is their ethnicity, their household size and income, etc.? In many cases, profiling usually follows clustering/segmentation. Who are the customers in Group 1?

Signing off with:
"There must be some kind of way out of here,"
Said the joker to the thief
"There's too much confusion,
I can get no relief"
-- All along the watchtower by Jimi Hendrix