In this post you will find a list of common the data fallacies that lead to incorrect conclusions and poor decision-making using data. Here you will find great resources and information so that you can always be reminded of these fallacies when you're working with data.
The Ten Fallacies of Data Science. There exists a hidden gap between the more idealized view of the world given to data-science students and recent hires, and the issues they often face getting to grips with real-world data science problems in industry.
When people analyze the qualities it takes to be a good entrepreneur, we typically look at the existing population of successful entrepreneurs for clues. However, by limiting our sample just to this “surviving” group of entrepreneurs, we run the risk of survivorship bias.
There are certainly lessons we can learn from all of the entrepreneurs who have failed they are just much harder to find. Integrating that data into the story can help complete a much fuller picture.
