Day 15 - Understanding Outliers in Pandas: The 1.5×IQR Rule

Context:

While learning Pandas, I explored how outliers are detected and why they matter in data analysis. To make it fun, I imagined pandas in a jumping contest — most pandas jump within bounds, but one panda leaps way beyond… that’s an outlier!

What I Learned:

• Outliers are data points that sit far outside the normal range.
• Tukey’s Rule of Thumb: Any value beyond: Q1 − 1.5 × IQR OR Q3 + 1.5 × IQR is flagged as an outlier.
• This method is widely used for detecting anomalies in datasets.

Why It Matters for QA / AI Testing:

• Outliers can skew test results and model predictions.
• Detecting and handling outliers ensures data quality and reliable AI outcomes.
• Helps testers validate preprocessing steps in ML pipelines.

My Takeaway:

Outliers may look like Rocket Ronny in a jumping contest — rare but impactful. Knowing how to spot them is key for accurate analysis.