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You're faced with statistical challenges from other teams. How do you defend your project assumptions?

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When your project assumptions come under scrutiny from other teams, it's essential to stand your ground with a solid defense rooted in statistical reasoning. The ability to articulate and justify your assumptions using statistical tools and concepts not only bolsters your project's credibility but also demonstrates your competence in navigating the complex landscape of data-driven decision-making. As you face challenges, remember that statistics is not just about numbers; it's about making sense of data to inform strategic choices. With the right approach, you can turn criticism into constructive dialogue and use it to refine your project's foundation.

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Nadav Rakocz

ML/AI @ Optum | PhD in Computer Science | ex Meta

1 Clarity First

In defending your project assumptions, clarity is paramount. Begin by explicitly stating each assumption and the rationale behind it. For example, if you've assumed a normal distribution of your data, explain why this makes sense given the context of your project. By doing this, you're not only reinforcing the logical structure of your assumptions but also providing a clear starting point for discussion. It's crucial to communicate in a way that is accessible to all stakeholders, regardless of their statistical expertise, to ensure that your reasoning is understood and appreciated.

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2 Data Evidence

Your next line of defense is presenting robust data evidence. Gather the empirical data that supports your assumptions and present it in a clear, concise manner. This could involve descriptive statistics, such as means and medians, or visual representations like graphs and charts. When you show that your assumptions are grounded in the reality of the data, it becomes much harder for others to dismiss them. Moreover, data evidence can serve as a common language, bridging gaps between different teams' perspectives.

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Nadav Rakocz

ML/AI @ Optum | PhD in Computer Science | ex Meta
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I would also include histograms and generate simulated data to further support and validate our assumptions about the distributions. This additional analysis will help provide a clearer and more comprehensive understanding of the data's characteristics and behavior.

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3 Method Validation

To further defend your project assumptions, validate your statistical methods. If you've used a specific test or model, justify its selection by discussing its relevance and reliability in the context of your project's goals. For instance, if you've employed a regression analysis, explain how it helps in understanding the relationships between variables relevant to your project. By demonstrating that your methods are not arbitrarily chosen but are instead the result of careful consideration, you can strengthen the credibility of your assumptions.

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Nadav Rakocz

ML/AI @ Optum | PhD in Computer Science | ex Meta
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1. It is also crucial to present the uncertainty in your findings, either through statistical bounds or by using simulated data. This approach will enhance the robustness of the decision-making process and provide additional support for the underlying assumptions. By explicitly addressing the uncertainty, we can ensure that our conclusions are more reliable and well-founded. 2. Conducting a sensitivity analysis is also very useful for hidden variables that can't be observed. Sensitivity analysis helps to illustrate the uncertainty caused by these unknown confounders. By examining how changes in unobserved variables affect the results, we can better understand the potential impact of these hidden factors and make more informed decisions.

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4 Peer Review

Engaging in peer review is a powerful way to defend your assumptions. Solicit feedback from colleagues who can critically evaluate your approach and offer insights. This collaborative process not only helps identify potential weaknesses before others do but also allows you to refine your assumptions with input from a broader range of expertise. A peer-reviewed project stands on firmer ground when facing external challenges, as it has already withstood scrutiny from within your own ranks.

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Nadav Rakocz

ML/AI @ Optum | PhD in Computer Science | ex Meta
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Try to present your work as early as possible by taking advantage of workshops and tutorials. This approach will help you receive valuable feedback and create opportunities for collaboration. By sharing your progress and ideas in these settings, you can refine your work based on the input from others and potentially form partnerships that enhance your research and development efforts.

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5 Alternative Scenarios

Consider alternative scenarios and how they might affect your project. This involves thinking about what could happen if your assumptions were incorrect and planning for those contingencies. Discussing these scenarios openly shows that you've thought through the potential risks and are prepared to adjust your approach if necessary. This level of foresight demonstrates a mature understanding of statistical analysis and its application to real-world projects.

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6 Continuous Learning

Finally, embrace continuous learning as a defense strategy. Stay abreast of new statistical techniques and industry best practices that could impact your project. By showing a commitment to ongoing education and improvement, you signal to others that your assumptions are not set in stone but are instead informed by the latest developments in the field. This openness to learning and adapting can help mitigate criticism and foster an environment of mutual respect and collaboration.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rafael Matos, MSc.

Data Tech Manager at Neon | Credit Risk Modeling | IFRS 9 | Loan Loss Provisions | Quantitative Finance | Retail Banking | Machine Learning | Data Science | Complex Systems MSc.
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Going to add the same advice I wrote in a similar article: Appropriate statistical metrics should be used to assess validity of model results… but not only! Also consider: 1. Particular case studies: evaluate the results for a couple of instances in order to understand whether they make sense; 2. Business metrics: defend your model through the lens of what really matters for the business decision makers; 3. What-if analysis: build simulations to have a grasp on what the results would be under different scenarios, assumptions or other criteria. But avoid overkill: focus on the important variations that usually need major business decisions.

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You're faced with statistical challenges from other teams. How do you defend your project assumptions?

1

2

3

4

5

6

7

1 Clarity First

2 Data Evidence

3 Method Validation

4 Peer Review

5 Alternative Scenarios

6 Continuous Learning

7 Here’s what else to consider

Statistics

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You're faced with statistical challenges from other teams. How do you defend your project assumptions?

1

2

3

4

5

6

7

1 Clarity First

2 Data Evidence

3 Method Validation

4 Peer Review

5 Alternative Scenarios

6 Continuous Learning

7 Here’s what else to consider

Statistics

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感谢您的反馈

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