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> Having trouble following what they are saying... the specific terminology over when mileage records were taken versus when autopilot was installed is confusin

> Having trouble following what they are saying... the specific terminology over when mileage records were taken versus when autopilot was installed is confusing I think this is one of the main points of the article. The NHTSA had the same data, and made a very sensational graph, without any clarification on what the headings mean. The authors essentially recapitulated the initial analysis and then figured out its shortcomings. > They identify a subset of vehicles and run an analysis on just those vehicles which seems to determine airbag deployments increased for those cars The article clearly explains why that subset was the most important. This was the only subset for which complete data was available. > and then run a regression that states the more miles driven the less likely an airbag is to deploy. They state this is unexpected but don’t explain what was wrong with their data to cause it. Actually, they do offer explanations in their discussion. They even refer the reader to the discussion right after they post the table. (i.e., See more on this topic in the Discussion section). Also, see my initial point; this is not 'their data' so much as what was relied upon by NHTSA to make the 2016 claim about autosteer safety. > Previous Mileage before Autosteer Install” and “Next Mileage after Autosteer Install” mean, and what it means when they are not reported, it’s hard to say. See my first point. However, they show pretty reasonably that those two headings could mean 'recorded mileage right before the update' and 'recorded mileage right after the update'. > Obviously a car with zero miles can’t have crashed, so this is what calls the entire calculation into question. This is another one of their points. They state multiple times that, if the data can be trusted, it doesn't support the NHTSA's claim. -- The article is actually very careful on all the points you raised, and would merit a close reading before being dismissed.

manicdee 8y ago
The NHTSA were using different data, since they had the non-redacted version. The author uses the redacted version, doesn’t attempt to find an explanation for anomalies, proceeds to analyse data based on faulty initial assumptions, and compounds this by using a cherry picked example where the poorly understood numbers best match their faulty assumptions. When there is no explanation for “airbags deployed before 0km on odometer” there is no point analysing the rest of the data. It’s all bogus, especially the stuff that looks like it makes sense. The fundamental fault of the author’s analysis is trusting data that is proven untrustable. In the same set of data which has some cars deploying airbags before ever being driven, the author assumes that the data which matches their model of validity is magically more accurate and thus worthy of study. The author continues to trust this data because it supports their biases, even while pointing out the errors in the rest of the data. How is this not cherry picking? The entire study is bogus because it starts with bogus data and attempts to make claims about reality based on a subset of bogus data that can be misinterpreted as less bogus. It’s like picking the few pages of manuscript from the infinite monkeys room which have meaningful phrases on them and claiming that obviously monkeys are really good writers.

manicdee 8y ago

The NHTSA were using different data, since they had the non-redacted version. The author uses the redacted version, doesn’t attempt to find an explanation for anomalies, proceeds to analyse data based on faulty initial assumptions, and compounds this by using a cherry picked example where the poorly understood numbers best match their faulty assumptions. When there is no explanation for “airbags deployed before 0km on odometer” there is no point analysing the rest of the data. It’s all bogus, especially the stuff that looks like it makes sense. The fundamental fault of the author’s analysis is trusting data that is proven untrustable. In the same set of data which has some cars deploying airbags before ever being driven, the author assumes that the data which matches their model of validity is magically more accurate and thus worthy of study. The author continues to trust this data because it supports their biases, even while pointing out the errors in the rest of the data. How is this not cherry picking? The entire study is bogus because it starts with bogus data and attempts to make claims about reality based on a subset of bogus data that can be misinterpreted as less bogus. It’s like picking the few pages of manuscript from the infinite monkeys room which have meaningful phrases on them and claiming that obviously monkeys are really good writers.