I personally disagree with the quote and firmly believe the other way “If you slice and dice the data in unbiased manner, it will reveal the truth.” One can create an extremely robust model where the results […] Even in the hands of someone benevolent, data can be misinterpreted in dangerous ways. The proliferation of new data-hungry apps, auto-play videos on social channels and the availability of super-fast 4G LTE networks have had a direct impact on the amount of data consumers use. There are other things that can cause data to be misinterpreted if you’re not aware of and work to avoid them. Comment and share: Top 5 biases to avoid in data science By Tom Merritt. Confirmation bias is where data scientists use limited data to prove a hypothesis that they instinctively feel is right (and thus ignore other data sets that don’t align to this hypothesis). Numbers don't lie but their interpretation and representation can be misleading. Authors have broad latitude when writing their reports and may be tempted to consciously or unconsciously “spin” their study findings. However, publication is not simply the reporting of facts arising from a straightforward analysis thereof. How to Avoid The Pitfalls of Misleading Data. There are three components required to make an expert business decision based on data : Statistical knowledge/ Quantitative aptitude Domain Knowledge Business Context To make data driven decisions using a mathematical approach, it is important to have a perfect blend of all the above factors. Or when people force fit data to what they already believe. OUTLIERS If you’re attempting to create a predictive model based off of your data, outliers can significantly skew the results leading to an unrealistic picture of what you should expect to achieve in the future. Here I show how to avoid misinterpretation and how to best proceed with answering the recent debate about sexual dimorphism in digit ratio, a trait that is thought to reflect sex-hormone levels during development. Publication in peer-reviewed journals is an essential step in the scientific process. – Ronald Coase, Economist. By using the standard model for visual models, you can avoid misleading your reader. By obscuring data or taking only the data points that reinforce a particular theory, scientists are indulging in unethical behavior. The best course of action with Simpson’s paradox (and, in fact, with any statistical data), is to use the information to refer back to the story of the data. Ethics in statistics are very important during data representation as well. A popular quote on the subject says: If you torture the data long enough, it will confess. If you want your data to tell the whole truth and nothing but the truth, implement these practices to make sure you avoid misleading data visualization. Asking “why” repeatedly before you settle on an answer is a powerful way to avoid … Spin has been defined as a specific intentional or … Data without facts gives you a two-dimensional, black-and-white view of the world. Someone who wants to win an argument using data can usually do so. Follow Convention. Tom is an award-winning independent tech podcaster and host of regular tech news and information shows. “I like data because it helps me win arguments” – Never has a phrase better revealed someone who doesn’t get value from data — Andrew Anderson (@antfoodz) January 6, 2015 7 common biases of Big Data analysis. There are essentially seven common biases when it comes to big data results, especially those in risk management.