In reply to Jarno Lindqvist.

Thanks Jarno! Absolutely! So much of gathering new insights comes from cleaning data and removing known erroneous values and hoping that the majority of your other values make sense. This is why domain knowledge is so important: unless you know a subject really well, it can be really difficult to know whether a value makes sense or not. For example, I initially thought my 62mph speed was plausible, but after delving into where it was captured and the speeds before/after it, I know that it's nearly impossible to go that fast in that particular location on the slope. If I didn't know anything about that slope, I would have needed to do other calculations to estimate how steep that section is, and even then, those data points may have errors. But with my own experience on that slope I can shortcut all that, look at it and say "Yep, that doesn't make sense!" For all other strange values that might be lurking in the data, using robust techniques is a good way to reduce the effects of outliers, especially when it comes to statistical modeling and reporting.

For anyone who is a data scientist consultant reading this: always confirm odd values with your customer. They'll be able to tell you very quickly if something seems off or is worth investigating further. Your domain experts are your best friend.