How to be wrong with Data | Maavrus

Written by Ameay Kumar

April 5, 2022

How to be wrong with data

In every aspect of our lives, we either validate our decisions based on data or ask people to support what data they are saying with data. You look at the rating of a movie before you decide whether to watch it or not. We do the same thing with restaurant choices. However, with all the data and quantification techniques available to us we still get things wrong on a recurring basis. There are multiple reasons for why this would happen and what are some of the most common fallacies around data.

Table of Contents


To start out simple fallacies are common errors in reasoning that will undermine the logic of your argument. There are quite a few fallacies that commonly occur when we interpret, analyze and visualize data. Here are the most common ones:

Cherry Picking– we have cherry-picked the first fallacy to start with because it is the most common cause of erroneous reasoning. In simple language, cherry-picking is using the data that best suits your narrative and ignoring/excluding anything that would indicate otherwise. One example of cherry-picking is when our beloved politicians talk about the GDP growth in a specific year as a result of their efforts- conveniently forgetting to mention this growth vs. a de-growth vs the previous year. A common element used with cherry-picking is called Occam’s Broom- this is when we use a broom to sweep the data that contradicts us under the carpet. This is probably the most common fallacy but not as deadly as our next one

The Cobra EffectAlso known as the perverse incentive. This fallacy and its name come from something that occurred in India during the British occupation. While it is very tempting to share the complete story here- we will resist that temptation but you can read it here. With the cobra effect fallacy, you arrive at behavior that you would like to incentivize, and yet this effort drives the complete opposite results. This one has some really fun examples 

The False CausalityThe rise of ice-cream sales correlates with the rise of people drowning in swimming pools. If you were looking at the trend lines you would be absolutely right- but this analysis does not take into account the fact that the rise in ice-cream sales & rise of people drowning are both independent of each other but dependent on the temperature going up

Those are our favorites but the list is quite extensive with other fallacies like- data dredging, overfitting, Hawthorne effect, McNamara effect, and Gerrymandering.

Related Articles

Customer feedback analytics to influence D2C product development​

Customer feedback analytics to influence D2C product development​

Hi. Welcome to expert talks at Maavrus, in this video, we’ll talk about how Direct to Consumer or D2C businesses can better understand their customer feedback data and leverage it to develop their existing and new products. https://www.youtube.com/watch?v=bd4PqAKjfTQ...

Analytics to grow Digital Commerce and Improve Digital Marketing ROI

Analytics to grow Digital Commerce and Improve Digital Marketing ROI

Hi. Welcome to Expert Talks at Maavrus, in the previous video, we spoke about why business leaders and marketing teams will need to have a mindset shift in the way they look at the effectiveness of their marketing spends. https://www.youtube.com/watch?v=SfwtRtjaQWk...


Submit a Comment

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