More data is always beneficial, something that most scientists look for is solid data.
A more useful approach would be to populate data of 50 or 100 users for each brand and then segment it into different component failure and then start the analysis.
If you would like to gain name and credibility as a source of reliable information, then providing the source of data with statistical analysis in the form of histograms or charts would be helpful.
This provides the readers deeper perspective, not just a number.
It would be wise to populate baseline data points for each brand, no need to rush.
Thanks for the tips Ravi! I’m used to working in much larger datasets in my day job as a mechanical engineer. Due to the limited dataset, the amount of stats possible was limited. I still feel that this data is valuable information for folks interested in ebike reliability.
From my day job, I regularly present complex data sets to folks that aren’t engineers. They need a simple result to understand, at least at first. Some folks, like you and me, would prefer the complete dataset so you could make your own evaluations. Most folks aren’t like that though and I wanted to present a ranking and mileage per failure that would be easily understood.
Perhaps with my next survey, I’ll have enough responses to populate 100 or so for each brand and start the analysis there. Thanks again, with these suggestions, the survey will improve continuously with each iteration.