What are the steps to creating a clear problem statement for data analysis?

Absolutely, defining a clear problem statement is crucial in data analysis and research. It helps focus your efforts and ensures that the data you collect and analyze will provide actionable insights. Starting with a specific, relevant, and actionable problem statement like the one you provided allows for a more effective and efficient data-driven approach.

Clearly defining the scope of your analysis is essential to prevent scope creep, maintain clarity, and ensure your analysis remains manageable. By specifying the data sources, time period, target population, variables, and assumptions upfront, you establish a framework that guides your entire data analysis process effectively. This ensures that your findings and conclusions are both meaningful and feasible within the defined boundaries.

Defining the scope is a very important step but it's not the second step. Before setting boundaries and limitations on your analysis, first you should have a clear idea/objective for why you are doing the analysis. After you have a clear picture of the objectives, you can continua with the scope.

This should come before defining the scope. Without getting a clear objective(s) first, you could stay stuck at defining the scope boundary as it is typically driven by the objective(s) of the analysis

Absolutely, stating a clear objective is crucial in data analysis. It provides a clear direction for your work, helps you stay focused, and sets the criteria for success. By expressing what you aim to achieve or learn from your analysis, you ensure that your efforts are purposeful and that your findings will be valuable in addressing the defined problem statement. It also helps in effectively communicating the goals of your analysis to stakeholders and team members.

I agree with the previous speaker. The analyst should have his/her objectives very clear before defining the scope of actions.

One thing I've found helpful is to acknowledge "no study question will help you attain true causal relationship using an observational data". That being said, if you intend to determine close to true causal relationship through data analysis using real world observational data the key factors to inculcate in formulating your questions are: 1- Acknowledge your analysis is not causal but rather attempting to achieve close to true causal by using words such as "affect", "associated" rather than "cause" 2- consider using vague terms as independent variables like "factors" instead of naming particulars (let data decide what factors are measurable) but be specific to mention outcome variables which will help gauge statistical analysis approach.

When getting feedback from yourself or others, try to approach it in a storytelling style. It’s easier to understand the problems that we aim to solve through narration

Add a sample scenario to further illustrates how this framework works. Problem solving always needs a clear narration and examples help.

Exclusion and inclusion criteria are important in case of creating problem statement for data analysis because they help to ensure that the data that is collected is relevant to the problem. Exclusion criteria are used to identify data points that should not be included in the analysis, while inclusion criteria are used to identify data points that should be included. For example, if you are trying to solve a problem about patient churn within healthcare settings, you might use exclusion criteria to remove data points from patients who stopped using a pharmaceutical product for more than 6 months. You might also use inclusion criteria to only include data points from patients who have made a purchase of that product in the past 12 months.