Webinar: Do you know your result is the right result?

Summary

We are all interested in collecting and analysing data as input to analysis, investigating problems or comparing results. In recent years it has become extremely easy to obtain transducers, data collection equipment and data analysis tools that are easy to use, generally are trouble free, and more importantly provide “instant” results and answers. Now there is nothing wrong with this, of course. Anything that makes Engineering easier, saving time/money and more consistent is great. However, as instrumentation and analysis become more like “black boxes” and analysis tools provide instant answers we also have to ask ourselves whether they are providing the best answer for me or the situation, or just an answer. A theoretically correct answer to the wrong problem is a case in point. Worse still is applying a default set of assumptions or settings to any problem. Rashly assuming what a problem is can often lead to a very good set of tests and analysis, that even if done correctly may not solve the problem. Similarly, there are many examples of data collection and analysis projects where two sets of engineers have produced very different results from the same data, test or analysis. I will use some simple real-world examples of very wrong results even when using very good equipment, and tools. There will also be some strategies and examples of how to ensure you can reduce the possibility of false results, and make sure your answers are believable and reproduceable.