“Correlation does not imply causation”. If you have ever done data analysis yourself then this phrase must be engrained in your brain. As over-used as this phrase seems it is probably not said enough. There is a reason for the popularity of the content about correlation vs causation (isn't there?). Simply speaking, correlation means there is a mutual relationship or

This is another one of our posts in the series of How Not to Get Fooled by Statistics. We talked about cherry picking data, visualisation tricks, and data sources. Sometimes misleading data can stem from the flawed research methodology. There are many ways where research could go wrong if the methodology is not thought through. We will try to review

Continuing our series of How Not to Get Fooled by Statistics our post today is about recognising misleading graphs or charts. Data visualisation is good, it helps us easily understand otherwise complex data and focus on what’s important. However, it is also a powerful tool for data manipulation and deception. There are so many misleading graphs and charts out there,

This post is continuing the series on How not to get fooled by statistics. We talked earlier about the importance of checking the data sources and their relevance. Today’s topic is about cherry picking data to present only those that are favourable to the presenter while omitting the rest of the information. That may sound harmless at first but if

In the previous post we started talking about misleading statistics and how they can be used to deceive people. Since we have a lot to say about it, we decided to make it a series of posts, so we can focus on different issues. Today we will talk about the importance of the data sources when dealing with statistics. When