Gather raw results systematically from laboratory experiments, ensuring proper documentation and standardized formats.

Remove errors, inconsistencies, duplicates, and outliers. This step ensures accuracy and reliability before analysis.

Structure the cleaned data into logical categories, tables, or databases for easier handling and retrieval.

Apply appropriate statistical methods (descriptive, inferential, regression, etc.) to identify patterns, relationships, and significance.

Cross‑check results with controls, replicate experiments if necessary, and confirm statistical robustness.

Translate statistical findings into meaningful insights that answer research questions or support hypotheses.

Present data through graphs, charts, and summaries to make results clear, accessible, and impactful.

Highlight the significance of findings, showing how they contribute to knowledge, innovation, or practical solutions.