Defining the problem statement is probably the most important aspect of any data science project’s life cycle. For any data science project to deliver any business impact, it’s important to understand the problem statement. Answers to the following 7 questions needs to be discovered and documented during the early stages of a project: 1. ELI5 problem statement. 2. One metric which we want to move and other metrics which we want to preserve? 3. What is the minimum expected improvement in the top metric? 4. Why is this a problem? 5. How is someone doing it currently and why a better solution is needed(if applicable)? 6. How will the end customer use it/how will it get integrated in the system? 7. What are the possible edge cases?

Leveraging Daniel Kahneman's insights on cognitive biases, statistics professionals can enhance decision-making by clearly defining the problem and objective. This approach helps avoid biases like confirmation bias and overconfidence, focusing analysis on data-driven, objective criteria. Such a disciplined, evidence-based methodology aligns with Kahneman's principles for rational, effective decision-making in complex scenarios.

Professionals can improve their decision-making by adopting a data-driven mindset, challenging assumptions, and being wary of overfitting. They should develop strong analytical skills, stay updated with statistical concepts, and learn new tools. Communicating your data properly is crucial, with clear explanations and collaboration with experts as every data tells some story. It's essential to anticipate and address concerns and collaborate with others to ensure insights are relevant and actionable. The iterative process is essential, as no statistical model is perfect. It's crucial to be prepared to revise models or conclusions as you learn more.

Statistics professionals can employ several strategies to enhance their decision-making skills: 1. Stay updated with the latest statistical methods, tools, and advancements. Engage in continuous learning through courses, workshops, seminars, and reading relevant literature to expand knowledge and skills. 2. Apply statistical concepts to real-world problems regularly. Practice working on diverse datasets and scenarios to gain experience in different applications of statistics. 3. Interact with peers, researchers, and professionals in the field to exchange ideas, discuss challenges, and learn from others' experiences. Collaborate on projects to gain insights and alternative perspectives.

Enhancing the efficacy of data-driven decision-making entails a nuanced consideration of four pivotal facets: 1. Data Literacy: Understand your data's nature, source, collection methods, and potential biases. 2. Statistical Knowledge: Learn about statistical methods and theories for appropriate tool selection and result interpretation. 3. Feedback and Adaptation: Continuously seek feedback and adapt methods or assumptions. 4. Data Manipulation and Exploration: Develop coding skills for efficient data reshaping, aggregation, and filtering to discover insights.

Data management starts from the sources of data. One of the major data biasedness starts from the source. Hence, it’s expedient to have a well laid out plan to carry out data collection if primary source or making sure of the validity and reliability of the secondary data. If that is taken care of, we can now start thinking of analyzing the data starting with data cleaning. The figure out the research questions to answer before proceeding to the analysis proper. One key thing in all of this is proper documentation prior the project management process and analysis

Empowering statistical professionals in decision-making involves strategic data gathering and analysis. Begin by establishing clear data collection methods aligned with the decision context. Scrutinize data for relevance, quality, and potential biases. Leverage exploratory data analysis techniques to uncover patterns and outliers. Utilize statistical models appropriate for the problem, fostering accurate predictions. Regularly validate and update models to ensure relevance. By employing rigorous data gathering and analysis strategies, statistical professionals enhance decision-making precision, providing organizations with reliable insights crucial for informed and impactful choices.

An often neglected but crucial step in collecting and analyzing data is to ensure the data is not underpowered for the selected analysis. Underpowered results can be misleading, and even though they may offer confidence in the moment of decision making, they’re likely to lead to long term challenges. A related issue is under sampling various conditions or groups one is comparing, especially when decision making requires covering data from various corners of a space. Such challenges can be overcome through Power analysis and strategic planning design of data collection.

In decision-making, statistical professionals excel by evaluating alternatives and risks. Conduct thorough sensitivity analyses to gauge the impact of varying assumptions. Employ statistical models to simulate potential outcomes, highlighting uncertainties. Quantify risks and probabilities to inform decision-makers effectively. Embrace Bayesian methods for robust uncertainty quantification. By rigorously assessing alternatives and risks through statistical lenses, professionals contribute essential insights, fostering informed decisions that navigate uncertainties with precision and strategic foresight.

Due to efforts being made originally statisticians tend to easily ignore alternative. One key skill to possess as a statistician is curiosity. What is the data saying, is this the best I can get from this. Think of BLUE if it’s a linear model, think of UMVUE make sure you have alternative methods and decide based on on the one that gives the smallest variation.

You could make ad hoc metrics to evaluate the different alternatives. In this way you will choose your final strategy in an objective way, taking into account possible bias and risks.

We have been taught in a particular way in school hence communicating our results and making justification aren’t really a strong part of most statistician. There is a big need to be able to communicate those numbers, models, visualizations such that stakeholders with little or no knowledge of the field can grasp. This comes with practice with colleagues that are not in your department, if you can get them to understand the concept then you’re in good shape to present to bigger audiences. Pilot with two or three people before doing the actual presentation

In some situations, it's important to make complex results easier to understand. This helps decision-makers quickly get the main points without getting lost in details. To do this effectively, it's essential to communicate clearly, focusing on what matters most for the decisions they need to make.

Once you make a decision it is important to work out the consequences. Design thinking works effectively in understanding how your actions affect others. Constantly track success, failures and setbacks of yiur decisions. A wise person learns from their mistakes. Be open to change and a dynamic personality helps in keeping up with the change.

As a manager you should have a decisionmaking mindset. Prolonged delays and sharing your burden on to someone else is not sustainable in the long run. Your decisionmaking ability builds your credibility. This helps a lot when you are in a leader, other rely on you. But it is equally important to put in your hardwork analyse costs and benefits to every action. Do your homework before taking any decision. This will help you withstand your decision even when others do not hold your POV.

In my experience, decision making isn’t only based on how good your model performs but also on its compatibility with business objectives with justified interpretations. Using variables like ethnicity in a loan disposal dataset might positively affect your statistical parameters but negatively affect business objectives. So as a statistician, it’s not always about complex technical analysis but also requires sound social and business awareness. After all, models and analysis are built for the people, better decisions aren’t solely in your AUC-ROC plots, it’s very much beyond!