Can you give an example of a research project where your critical thinking was essential in solving a problem?

Sample answer to the question

One time in my last job, I had to figure out why our experiments were getting inconsistent results. Using critical thinking, I evaluated our data collection methods. I realized that the temperature fluctuations in the lab could be messing with our enzyme reactions. So, I kept a log of temperatures throughout the day, correlated them with our test results and bingo, that was it! We implemented a new temperature control system, and the problem was solved, leading to more reliable data.

A more solid answer

In my previous role as a biochemist, I led a project analyzing the impact of certain drugs on cellular aging. During one experiment, we were seeing odd variances in the cell senescence markers. I had to dive deep, critically examining each step of our procedure and the data we collected. My statistical analysis, using R for complex regression models, indicated a pattern that I initially couldn't explain, until I realized it might be contamination during cell culture. By redesigning our sterilization process and rerunning the experiments, we not only fixed the issue but ended up refining our entire experimental protocol, which enhanced our overall research effectiveness.

Why this is a more solid answer:

The solid answer elevates the detail, mentioning a specific field (biochemistry), utilizing statistical software (R), and an in-depth reaction to unexpected data. It provides a better demonstration of experimental design and critical analysis of methodologies. However, it could delve more into how this enhanced critical thinking and adaptability and what technologies were implemented or modified.

An exceptional answer

At InnovTech Pharma, I oversaw a research project on genetic predictors for drug efficacy. We were stumped by a set of inconclusive results, which led to my team and I conducting a thorough statistical analysis. Leveraging advanced data analysis tools like Python's SciPy and Pandas, we employed a variety of statistical models to interpret our genetic sequencing data. It dawned on me to reassess our control conditions, which prompted me to collaboratively redesign our experiment alongside my colleagues, factoring in more stringent controls and innovative sequencing methods. By integrating a novel bioinformatics tool I had recently become proficient in, we were able to eliminate confounding variables. This critical approach not only salvaged the project but also contributed to two peer-reviewed publications and optimized our research methodology for future studies under stringent timelines.

Why this is an exceptional answer:

The exceptional answer incorporates all required evaluation areas by mentioning the use of advanced data analysis tools and collaboration on experimental design. It demonstrates strong critical thinking through a detailed account of problem-solving and data interpretation. The answer also highlights improved research techniques, alignment with job responsibilities such as publishing and mentoring, and shows initiative in learning new technologies, tackling a complex problem within the stipulated experience level.

How to prepare for this question

Reflect on specific instances where you effectively used critical thinking to solve research problems. Be sure to consider projects that required statistical analysis and a deep understanding of experimental design.
Familiarize yourself with statistical programming tools, like R and Python, and be ready to discuss how you have used these tools in problem-solving scenarios.
Consider how you can demonstrate adaptability in your answer, perhaps by showing how you've learned and applied new technologies or methodologies quickly
Highlight collaboration by detailing experiences where you worked with others to overcome research challenges.
Prepare to speak about how solving the problem helped improve your time management and organizational skills in the context of completing research projects.

What interviewers are evaluating

Critical thinking
Statistical analysis and data interpretation
Experimental design
Adaptability to new technologies and methodologies