Rubric for Statistics and Experimentation Questions
While the format and structure of these interviews vary depending on the company and role, core rubric signals interviewers use include:
- Statistics fundamentals: knowledge of key concepts in statistics and experimentation
- Problem and hypothesis understanding: ability to articulate reasonable and well-defined hypotheses for key business problems, identify concrete requirements, and define the scope of an ambiguous problem
- Real-world application: ability to identify the correct statistical technique(s) to use in a practical setting, and communicate the pros and cons of different techniques
- Communication and data interpretation: ability to clearly communicate methodology and insights to different stakeholders, including non-technical stakeholders
- Bias and confounding factors: awareness of potential biases and confounding factors that could impact the experiment’s validity and ways to mitigate them
The overall rating of the rubric signals translates to:
- Very Weak: no hire.
- Weak: no hire, but interviewer can be convinced if candidate did exceptionally well in other interview rounds. Can lead to “downleveling.”
- Strong: hire, but interviewer may be convinced to no hire if candidate did poorly in other rounds.
- Very Strong: strong hire, interviewer will advocate for the candidate, even if other rounds went poorly. Can lead to “upleveling.”
Below, we’ll define what a very weak to very strong answer looks like for each rubric signal.
Statistics fundamentals
- Very Weak: Provides incorrect answers and does not demonstrate a good understanding of basic statistical concepts.
- Weak: Answers some questions correctly but most responses are vague or generic, demonstrating a surface-level understanding of concepts. Fails to answer follow-up questions accurately.
- Strong: Answers most questions correctly with clarity and specificity. Shows a deep understanding of concepts by answering follow-up questions adequately.
- Very Strong: Provides multiple detailed and structured possible solutions. Able to discuss the pros and cons of each solution.
Problem understanding and hypothesis formulation
- Very Weak: Does not ask clarifying questions to better understand the problem space before proposing solutions. Fails to identify the actual problem or provide relevant hypotheses. Provides vague or overly broad statements that don’t provide clear direction for the analysis. Unable to incorporate suggestions from the interviewer effectively.
- Weak: Asks some questions to understand the problem space but doesn’t integrate the interviewer’s responses. Starts solutioning with a surface-level understanding of the problem and without a well-defined scope**.** Provides some hypotheses but lacks specificity, clarity, and relevance to the problem at hand. Does not tie hypotheses to the company’s mission or key goals.
- Strong: Asks relevant clarifying questions to understand the problem space, identifies requirements, and defines a clear scope. Provides relevant hypotheses, demonstrating domain knowledge and understanding of the business problem.
- Very Strong: Provides multiple well-defined and testable hypotheses that are grounded in domain knowledge, supported by data, and related to the company’s mission/key goals. Makes reasonable assumptions and states them clearly. Proactively defines the scope, requirements, and constraints of the problem. Effectively incorporates feedback from the interviewer. Provides a clear analysis plan to test the hypotheses.
Weak: “We hypothesize that changing the color of the ‘Sign Up’ button will improve user engagement.”
Strong: “We hypothesize that changing the color of the ‘Sign Up’ button from blue to green will increase click-through rate by Y%, as green is associated with positive actions from User Research studies, and has been shown to increase conversion rates from previous experiments. Increasing sign-ups contributes towards the key business goal of increasing MAU (Monthly Active Users) to X.”
Real-world application
- Very Weak: Fails to translate the business problem correctly into a data science problem. Unable to identify the optimal statistical technique or solution to use. Sticks to a specific solution and is unable to change course, even when the interviewer provides hints.
- Weak: Identifies the correct high-level technique to use but fails to consider the nuances of the specific problem/scenario. Tries to apply a standard formula or framework without adapting it to the problem. Does not provide a meaningful interpretation of results.
- Strong: Proposes at least 2 solutions or statistical techniques that can be used in the scenario and discusses the pros and cons of each. Demonstrates a clear understanding of the problem, the reasoning behind the proposed solutions, and potential outcomes or the impact.
- Very Strong: Demonstrates an understanding of the variables involved and their potential impact on the outcome of interest. Identifies multiple potential solutions with pros and cons in a structured manner. Weighs trade-offs and considers the long-term effects of each solution. Includes relevant metrics or results to quantify the impact of the solution. Discusses practical considerations such as implementation cost, feasibility and risk.
Communication and data interpretation
- Very Weak: Uses unnecessary technical jargon without explaining the meaning or relevance. Unable to communicate statistical concepts clearly. Fails to provide context or real-world implications of statistical findings.
- Weak: Communicates statistical concepts, but struggles to provide clear insights and real-world implications of findings. Draws broad conclusions without acknowledging limitations or uncertainty.
- Strong: Uses minimal technical jargon and references relevant examples to communicate complex statistical techniques in a clear manner. Provides real-world implications of statistical results and actionable insights.
- Very Strong: Inquires about the audience and tailors communication to their technical skills. Provides a succinct summary and key takeaways before diving into the details. Ensures that findings are accurately interpreted and understood by a wide audience, to facilitate informed decision-making. Recognizes and addresses limitations or uncertainties in the analysis.
Let’s say you’re asked to “summarize your methodology and findings in a statistical case study to a non-technical stakeholder, like a Product Manager.” Note the prevalence of technical jargon in the weak answer, compared to the strong.
Weak: “Imagine we're conducting a multivariate analysis of covariance (MANCOVA) to examine the effect of our independent variables on the dependent variable while controlling for covariates. Now, in order to ensure the validity of our findings, we need to assess the assumption of homogeneity of regression slopes. This is crucial because violations of this assumption can lead to biased estimates and erroneous conclusions in our analysis. Therefore, we'll employ various diagnostic tests, such as the Levene's test and the Box's M test, to evaluate the equality of regression slopes across groups and ensure the robustness of our statistical model.”
Strong: "We're checking to see if the relationship between our variables stays the same across different situations. This matters because if it doesn't, our results might be wrong. We'll run some tests to make sure everything's consistent and our analysis is robust. I’m happy to provide more details on this analysis if desired."
Bias and confounding factors
- Very Weak: Overlooks the possibility of bias altogether or provides superficial acknowledgment without addressing it effectively.
- Weak: Mentions some biases and confounding factors but fails to demonstrate a thorough understanding of how bias impacts the analysis and its outcomes.
- Strong: Thoroughly explains relevant biases and confounding factors, as well as how they impact the analysis. Proposes strategies to mitigate these biases.
- Very Strong: Proactively acknowledges the complexities of bias in data, identifies potential sources of bias that are specific to the problem at hand, and proposes specific techniques or methodologies to mitigate or account for it in the analysis.
Additional criteria
Depending on your background and specific roles, your interviewer may include other signals, such as:
- Time series data analysis: ARIMA, time series decomposition, stationarity, ACF, and PACF. These topics are relevant for roles in finance and forecasting.
- Markov chains: This is relevant for roles in finance, economics, and Natural Language Processing (NLP).
- Machine learning: logistic regression, tree-based models, clustering, and PCA. These topics are relevant for roles focused on building machine learning models.
- Data visualization: histograms, box plots, Sankey charts. These topics are relevant for roles in business intelligence and analytics.