INTERMEDIATE LEVEL

Can you describe a situation where you made a mistake in your data analysis? How did you rectify it?

Sample answer to the question

Yes, I have encountered a situation where I made a mistake in my data analysis. It was during a research project where I was analyzing genomic data to identify potential genetic variants associated with a particular disease. I mistakenly used the wrong statistical analysis method, which led to incorrect results. However, as soon as I realized my mistake, I took immediate action to rectify it. I consulted with my supervisor and bioinformatics team members to discuss the issue and determine the correct analysis approach. We reanalyzed the data using the appropriate method and obtained accurate results. This experience taught me the importance of double-checking and verifying the analysis methods to ensure accurate and reliable results.

A more solid answer

Certainly! I had an experience where I made a mistake in my data analysis during a genomic research project. We were investigating the genetic factors contributing to a complex disease. In one particular analysis, I had incorrectly applied a statistical model that was more suitable for binary data instead of continuous data. As a result, the interpretation of the results was misleading, and it could have led to incorrect conclusions if not rectified. However, upon realizing the mistake, I immediately informed my supervisor and bioinformatics team. Together, we reviewed the analysis pipeline, identified the error, and reanalyzed the data using the appropriate statistical model. The corrected analysis revealed significant genetic associations that aligned with existing knowledge on the disease. This experience taught me the importance of thoroughly understanding the statistical models and their applicability to the data at hand, as well as the significance of collaborative problem-solving to rectify mistakes.

Why this is a more solid answer:

The solid answer expands on the basic answer by providing specific details, including the type of mistake made (incorrect statistical model usage), the potential impact of the mistake, the collaborative approach taken to rectify the issue, and the positive outcome of the corrected analysis. The answer demonstrates the candidate's analytical and problem-solving capabilities, as well as their attention to detail.

An exceptional answer

Certainly! During a genomics research project, I encountered a mistake in my data analysis that required immediate rectification. We were studying the gene expression patterns in a specific tissue and had obtained RNA-seq data from multiple samples. In the initial analysis, I accidentally used the wrong normalization method, which resulted in biased expression values and distorted downstream analysis. Recognizing the error, I took swift action to rectify the issue. I consulted with domain experts and bioinformatics colleagues to review the analysis pipeline and identify the correct normalization method. Following their guidance, I reprocessed the data using the appropriate method and performed the downstream analysis again. The revised analysis revealed novel gene expression patterns related to the biological process we were investigating. Moreover, I realized the significance of thorough data quality control and documentation as preventive measures to avoid similar mistakes in the future. This experience reinforced my commitment to attention to detail, continuous learning, and collaboration in the field of genomics data analysis.

Why this is an exceptional answer:

The exceptional answer further enhances the solid answer by providing additional specific details, such as the specific type of data (RNA-seq) and the consequences of the mistake (biased expression values). The candidate also highlights the collaborative approach taken and the positive outcome of the corrected analysis, including the discovery of novel gene expression patterns. Additionally, the answer showcases the candidate's commitment to continuous learning and improvement in genomics data analysis.

How to prepare for this question

Familiarize yourself with various statistical analysis methods commonly used in genomics data analysis and understand their applicability to different data types.
Be proactive in seeking feedback and guidance from domain experts and bioinformatics colleagues to ensure accurate data analysis.
Develop a habit of double-checking and verifying analysis pipelines to minimize the likelihood of mistakes.
Emphasize the importance of continuous learning and staying updated with the latest advancements and methodologies in genomics research and data analysis.

What interviewers are evaluating

Analytical and problem-solving capabilities
Attention to detail