What are some common mistakes in exploratory data analysis?
An example of an AI chatbot conversation about common EDA mistakes in data analysis.
What are some common mistakes in exploratory data analysis?
Some common mistakes in EDA include not visualizing data, ignoring outliers, and failing to check assumptions.
Can you explain why visualizing data is important?
Visualizing data helps to identify patterns, trends, and anomalies that might not be obvious in raw data.
What should I do about outliers?
Outliers should be investigated to determine if they are errors, valid extreme values, or if they provide insights.
How can I ensure I'm checking assumptions properly?
You can use statistical tests and visualizations like Q-Q plots to check assumptions about your data.
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