Quantitative Research & Data Analysis - How to Handle Statistical Outliers Without Bias

🎓 1-ON-1 HELP (FREE CONSULTATION): https://gradcoach.com/?utm_source=YT&utm_campaign=u05x-9nUWfs 🧰 FREE RESEARCH TOOLKIT: https://gradcoach.com/toolkit/?utm_source=YT&utm_campaign=u05x-9nUWfs 📚 FREE EVENTS & WORKSHOPS: https://gradcoach.com/events/?utm_source=YT&utm_campaign=u05x-9nUWfs Not sure what to do with statistical outliers in your data? Worried that removing them might look like you’re manipulating your results? In this video, we explain when it’s okay to remove outliers and how to do it in a clear, defensible way. You’ll learn how to identify outliers using established statistical methods, how to justify your decisions, and how to report everything transparently in your dissertation. Timestamps 00:00 Can you remove outliers from your dataset? 00:09 Why removing outliers can feel risky 00:13 Yes, but only with proper methods 00:20 Why you need a standardized approach 00:23 The IQR method explained 00:40 How the 1.5 × IQR rule works 00:58 Don’t remove outliers based on intuition 01:04 How to justify outlier removal 01:14 Why you must cite your method 01:20 Be transparent in your write-up 01:30 The importance of reporting removed outliers 01:41 Consider norms in your field Outlier handling can make or break the credibility of your analysis. If you’re unsure how to approach it or explain it in your dissertation, we can help. 👉 Book a free consultation here: https://gradcoach.com/dissertation-coaching/