Correlation Heatmap

🔎 Research Interpretation

⚠️ Important Research Guidance

• Pearson's correlation measures the strength and direction of a linear association between two numerical variables.
• Correlation does not by itself establish causation.
• Extreme outliers can substantially influence Pearson's correlation coefficient.
• Very small sample sizes should be interpreted cautiously.
• For non-normal, ordinal, or monotonic relationships, researchers may consider Spearman's rank correlation.
• Identifier variables such as Replicate, ID, Sample ID, or Serial Number are excluded from correlation analysis.