Reproducibility is at the core of scientific research. It is what differentiates a true breakthrough discovery from a fluke. If a result is not reproducible, it casts doubt on the research. Sometimes this can even lead to a retraction of the research. But how can you ensure that your data is reproducible? The answer lies in your data collection strategy, i.e., your research methods.

If you are a scientist or engineer, then you will probably have to collect and analyze a large amount of data over the course of your career, be it in academia or in the industry. You need to select research methods that specifically describe how to collect valid data. This data then needs to be validated before it is published in a journal or a patent application.

It is well known that a large amount of research in the field of mechanical engineering is focused on numerical methods and simulations; however, analytical methods are equally important. Whether it is a stress test for a new material, a novel heat transfer mechanism, or an estimation of pressure drop performance, analytical methods are constantly in use in mechanical engineering. It is, therefore, essential to know how to validate your analytical methods.

Analytical method validation