Post by Felix Oyeleye (@DrFAO)
Ever wondered why two studies on the same topic can reach completely different conclusions? 🤔
The problem is often not the statistics… It’s the data.
💧In this video, we break down the hidden data issues that quietly sabotage research findings before analysis even begins. You’ll learn why missing values, dirty data, poor documentation, and biased samples can produce “significant” results that are actually misleading or wrong.
We’ll walk through:
The research pipeline: Data Collection → Cleaning → Validation → Analysis → Results
đź’§Common data problems: missing outcomes, skipped questions, dropouts, duplicates, and inconsistencies
đź’§Why completeness, cleanness, and integrity matter more than fancy statistical methods
đź’§Practical tips to improve data quality and protect your research credibility.
đź‘Ť If you found this useful, share with your students or colleagues, and like the video,
đź”” Subscribe for more clear explanations of research methods and statistics.
I look forward to your honest feedbacks.
#LearnWithFAO
https://youtu.be/ngyE6H9PPSc
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