Explanatory Modeling vs Descriptive Statistics

Developers should learn explanatory modeling when working on projects that require understanding why phenomena occur, such as in scientific research, A/B testing analysis, or business intelligence to inform decision-making meets developers should learn descriptive statistics to effectively analyze and interpret data in fields like data science, machine learning, and business intelligence, as it helps in data exploration, quality assessment, and communication of insights. Here's our take.

Explanatory Modeling

Nice Pick

Developers should learn explanatory modeling when working on projects that require understanding why phenomena occur, such as in scientific research, A/B testing analysis, or business intelligence to inform decision-making

Pros

• It is essential in fields like economics, social sciences, and healthcare, where interpreting model coefficients and assessing causal effects is critical for drawing valid conclusions and driving policy or strategy
• Related to: statistical-analysis, regression-analysis

Cons

• Specific tradeoffs depend on your use case

Descriptive Statistics

Developers should learn descriptive statistics to effectively analyze and interpret data in fields like data science, machine learning, and business intelligence, as it helps in data exploration, quality assessment, and communication of insights

Pros

• It is essential for tasks such as preprocessing data, identifying outliers, and summarizing results in reports or dashboards, making it a core skill for roles involving data-driven decision-making
• Related to: inferential-statistics, data-visualization

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Explanatory Modeling is a methodology while Descriptive Statistics is a concept. We picked Explanatory Modeling based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Explanatory Modeling wins

Based on overall popularity. Explanatory Modeling is more widely used, but Descriptive Statistics excels in its own space.