The Data Scientist’s Code: 10 Commandments for Ethical and Effective Data Analysis
The 10 Commandments of Statistics and Data Science
As statistics and data science continue to evolve and play an increasingly important role across industries, some guiding principles can help practitioners uphold scientific integrity. Here are 10 proposed commandments for ethically and effectively applying statistical methods and tools:
1. Thou shalt not hunt statistical significance with a shotgun. Do not try out multiple hypotheses and models solely to obtain a significant result. Carefully choose analytical methods based on theory and design.
2. Thou shalt not enter the valley of the methods of inference without an experimental design. Have a clear plan and structure for data collection and analysis before drawing conclusions.
3. Thou shalt not make statistical inference in the absence of a model. Ensure you have an appropriate statistical model to apply before making claims about a larger population or process.
4. Thou shalt honor the assumptions of the model. Check that your data meets the assumptions required for the statistical techniques you intend to use. Transform data if needed.
5. Thou shalt not adulterate thy model to obtain significant results. Do not tweak your model until you achieve statistical significance. Only make judicious, theory-driven model adjustments.
6. Thou shalt not covet thy colleague’s data. Respect proprietary data. Do not plagiarize or misuse data that is not your own. Give proper credit and attribution.
7. Thou shalt not bear false witness against thy control group. If using a control group, report differences honestly without twisting results.
8. Thou shalt not worship the 0.05 significance level. Do not treat statistical significance as definitive proof. Interpret p-values in context along with other metrics.
9. Thou shalt not apply large-sample approximations in vain. Use appropriate methods for small sample sizes and be cautious about generalizing findings.
10. Thou shalt not infer causal relationships from statistical significance. Correlation does not imply causation. Control for other factors that may influence effects.
Bonus Commandments
11. Thou shalt use visualization to explore thy data. Visualization is a powerful tool for understanding data. We can use visualization to identify patterns and trends in the data that we might not be able to see otherwise.
12. Thou shalt document thy code and analysis. It is important to document our code and analysis so that we can reproduce our results later and so that others can understand our work.
13. Thou shalt share thy findings with the world. Once we have completed our analysis, we should share our findings with the world. This will help others to learn from our work and to build on our findings.
Following principles like these can help uphold scientific principles and prevent questionable research practices. Data scientists should stay up to date on emerging standards and techniques while analyzing data ethically. With the growing influence of data science, following fundamental commandments can instill public trust in data-driven insights.
