"Simulation first" is how I did things when I worked in data science and bioinformatics. Define the simulation that represents "random", then see how far off the actual data is using either information theory or just a visual examination of the data and summary statistic checks. That's a fast and easy way to gut check any observation to see if there is an underlying effect, which you can then "prove" using a more sophisticated analysis.

Just raw hypothesis is just too easy to juke by overwhelming it with trials. Lots of research papers have "statistically significant" results, but give no mention of how many experiments it took to get them, or any indiciation of negative results. Eventually, there will always be the analysis where you incorrectly reject the null hypothsis given enough effort.