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What's the article about?
- It explores the significance of data-driven decision-making in today's world and the role of statistical analyses in drawing conclusions from data.
- It emphasizes the importance of domain expertise alongside statistical aptitude for maximizing the value of data.
Relevance:
- GS2: Governance;
- Essay
Analysis:
- Importance of data, key findings and analysis:
- Data or findings plays a very crucial role in the policy formulation and its implementation.
- However, how data is collected, what methodologies are used, context in which data is analysed or models that are applied all affect the outcome of statistical analysis.
- This is elaborated in the following sections.
- Analyses and Findings:
- Different methodologies can lead to contradictory conclusions from the same data.
- The statistical analyses rely on underlying models to explain data patterns, and decision-making heavily depends on these models.
- The statistical expertise, wisdom, and a solid understanding of reality are crucial for comprehending the nuances of data analysis.
- Anchored to a 'model':
- The statistical analyses are not uniform, especially when different methodologies are employed.
- It is important to understand the significance of understanding the causal relationships underlying a phenomenon as the basis for data analysis.
- For example, the gender bias data for admissions at the University of California, Berkeley, which revealed a statistically significant bias in favour of women.
- Another example which emphasizes the importance of statistical expertise, wisdom, and a solid understanding of reality in comprehending data is as follows: The Indian Statistical Institute (ISI) obtained multiple estimates of the number of refugees in the Red Fort during the communal riots in Delhi in 1947. The ISI team suggested using the quantity of salt as an estimate of the number of refugees.
Conclusion:
- Thus, we need domain expertise alongside statistical aptitude to extract the most value from data.
- By recognizing the importance of underlying models, statistical expertise, wisdom, and a solid understanding of reality, researchers and analysts can effectively interpret data and make informed decisions.
