The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

The 8 Logical Data Fallacies To Avoid In Data Analysis

February 15, 2017 at 3:17 PM

digital-thinker.png“Lies, damned lies and statistics” is the frequently quoted adage attributed to former British Prime Minister Benjamin Disraeli. The manipulation of data to fit a narrative is a very common occurrence from politics, economics to business and beyond. 

In this blog post, we'll touch on the more common logical fallacies that can be encountered and should be avoided in data analysis.

Logical fallacies in data interpretation

Statistics are simply numbers – how we (choose to) interpret them is up to us mere mortals and the key mental tool of critical thinking. It’s time to think about thinking.

A core competency of a data scientist is to be able to translate effects and patterns of data into real-life context. For business managers who need to periodically analyse data, an essential skill is ensuring that an interpretation is indeed correct and not tarnished by one of the many logical fallacies or mental mistakes that we all make in everyday life.

Philosophers have for some time understood the mistakes we make in reasoning. Aristotle is the first known philosopher to have established a list of logical fallacies. These fallacies are common mistakes made in arguing and thinking. An awareness of these is extremely helpful in sharpening one’s analytical ability. Developing awareness of thinking is known as metacognition and it is a key component to critical thinking. 

When it comes to analysing data or assessing a conclusion from the data or models presented, it is worthwhile to be aware of the reliability of what is presented. The reliability may vary due to the

Below is a list of the more common logical data fallacies to avoid in analytics - I'll by covering some of these in more detail in future blogs:

  1. The Cherry-picking fallacy of selectively choosing your data or statistics to prove your argument, or using confirmation bias and motivated reasoning instead of deductive reasoning in your analysis;
  2. The Texas Sharpshooter fallacy of looking for patterns, but ignoring contradictions;
  3. Correlation does not imply causation, or similarities between two statistics or trends does not imply that the one caused the other. Here are some funny examples of this;
  4. The Gambler's fallacy of looking at an unlikely string of events and implying that it will break; 
  5. The False Dichotomy or assuming/establishing a binary state when there is none;
  6. Lottery fallacy of questioning a result because it is highly improbable; 
  7. Simpson's Paradox illustrating how easy it is to misinterpret data by jumping to conclusions driven by motivated reasoning and not by objectively assessing the evidence.  
  8. P-hacking or identifying trends as statistically significant when they are not.

Subscribe to our blog to be notified of my future posts covering the above in more detail.

contact us

Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

Latest Posts

10 Machine Learning Books To Read For Budding Data Scientists

Machine learning and artificial intelligence are exciting fields, and we've been writing about these topics for a couple of years now. While a lot of what we talk about on our blog is advanced implementations of machine learning and can be overwhelming to beginners, the core concepts of machine learning are actually pretty easy to grasp. There are many resources and cheat sheets available online, but we believe the old fashioned way of learning is sometimes the best: with a good book. Few resources can match the in-depth, comprehensive detail of a good book.

Our Top Artificial Intelligence, Machine Learning And Data Science Podcasts

Our data scientists are keen readers and avid podcast listeners. In this blog, we list a few of the podcasts that cover topics such as data science, machine learning and artificial intelligence, and that we’d recommend if you’re looking to start exploring the world of podcasts.

Where To Learn Data Science Skills In South Africa

In this blog, we’ve created a (non-exhaustive) list of courses you should consider if you want to learn essential data science skills in South Africa. These courses are mostly classroom training from South African institutions, but if you’re more interested in online learning, check out our blog Where To Learn Essential Data Science Skills Online.