Data Analysis Through Inquiry

When it comes to data analysis, I adopt a comprehensive approach centered around three fundamental questions:

1.    How was the data collected? What methodologies were employed to gather the data, and how is it organized and structured?

2.    What prior studies or existing research can inform our understanding of the specific question at hand? By exploring related work, we can develop logical hypotheses and approach the analysis with a solid foundation.

3.    How can we leverage this data to drive improvement and enhance our understanding? While many rush to answer this question immediately, I believe in taking a step back and prioritizing a deep understanding of the data itself.

Through my experience working with both general experimental data and big data, I've observed a significant knowledge gap in comprehending the inherent biases and flaws present, particularly in large datasets. To address this, I advocate for beginning the analysis process by understanding the data collection process and identifying potential biases. This foundation enables us to conduct accurate analysis, make necessary adjustments, and minimize biases, thus yielding the most reliable and precise conclusions from our extensive data arrays.